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Öğe Accurate 3D contrast-free myocardial infarction delineation using a 4D dual-stream spatiotemporal feature learning framework(Elsevier, 2023) Liu, Jinhao; Zhu, Xinglai; Xu, Chenchu; Xu, Lei; Polat, Kemal; Alenezi, FayadhAccurate 3D contrast-free myocardial infarction (MI) delineation has the potential to eliminate the need for toxic injections, thereby significantly advances diagnosis and treatment of MI. In this study, we propose a 4D dual-stream spatiotemporal feature learning framework (4D-DSS) that enables learning of 4D (3D + T) representation of the heart to accurately map the 3D MI regions, thereby directly delineating of 3D MI without contrast agent. This framework creatively introduces a dual-stream 3D spatiotemporal point cloud architecture enables to learn the myocardial 4D representation in both local and global aspects, and improve the comprehension and precision of the representation. Specifically, the framework utilizes the local spatiotemporal variation of individual point clouds to characterize minute distortions in myocardial regions and the global spatiotemporal variation of point cloud sequences to represent the overall myocardial motion between frames, thereby enables comprehensive learning of 3D myocardial motion and leverages these features to classify myocardial tissue into MI regions and normal regions. 4D-DSS significantly improved performance (with a precision increase of at least 4%) compared to four advanced methods. The results support the impact of our 4D-DSS framework on the development and implementation of 3D contrast-free myocardial infarction region delineation technology.& COPY; 2023 Elsevier B.V. All rights reserved.Öğe An adaptive deep learning framework to classify unknown composite power quality event using known single power quality events(Pergamon-Elsevier Science Ltd, 2021) Sindi, Hatem; Nour, Majid; Rawa, Muhyaddin; Öztürk, Şaban; Polat, KemalDistributed generation (DG) sources are preferred to meet today's energy needs effectively. The addition of many different types of renewable energy sources to the grid causes various problems in signal quality. Detection and classification of these problems increase efficiency by both the producer and the consumer. In the literature, incredibly singular and some composite power quality disturbance (PQD) detection is performed effectively. However, the multitude of composite PQD variations degrades the performance of existing algorithms. In this study, the classification of all PQD variations that may occur is performed by using singular PQD and some composite PQD signals. A different number of subcomponents representing the signal are created according to each signal characteristic. Instantaneous energies from these subcomponents are used as deep learning (DL) input. Deep learning cycles are created as much as the instantaneous energy number of each signal. Each cycle has specific features of defining a single event. Therefore, the proposed approach is able to classify composite PQD signals that it has not encountered before. The proposed method's performance is first evaluated with the known PQD events and compared with the current state-of-the-art methods in the literature. Then, a dataset containing the combinations of different events not encountered during the training is created, and the performance is evaluated on this dataset. In the experiments performed, it is revealed that the proposed framework produces higher performance than other state-of-the-art methodsÖğe Aliasing black box adversarial attack with joint self-attention distribution and confidence probability(Pergamon-Elsevier Science Ltd, 2023) Liu, Jun; Jin, Haoyu; Xu, Guangxia; Lin, Mingwei; Wu, Tao; Polat, KemalDeep neural networks (DNNs) are vulnerable to adversarial attacks, in which a small perturbation to samples can cause misclassification. However, how to select important words for textual attack models is a big challenge. Therefore, in this paper, an innovative score-based attack model is proposed to solve the important words se-lection problem for textual attack models. To this end, the generation of semantically adversarial examples in this model is adopted to mislead a text classification model. Then, this model integrates the self-attention mechanism and confidence probabilities for the selection of the important words. Moreover, an alternative model similar to the transfer attack is introduced to reflect the correlation degree of words inside the texts. Finally, adversarial training experimental results demonstrate the superiority of the proposed model.Öğe Alternatively new signal for sleep staging processing in patients with obstructive sleep apnea: photoplethysmography signal(Ieee, 2016) Uçar, Muhammed Kürşad; Bozkurt, Mehmet Recep; Polat, Kemal; Bilgin, CahitDiagnosis of Obstructive Sleep Apnea is done by expert doctors by examining biological signals which is obtain from the patient help of polysomnography device. Review, consists of two stages which are sleep staging and respiratory scoring. Sleep staging is done using Electroencephalogram, Electromyogram and Electrooculogram signals. Derivation of signal format gives discomfort to the patient. In order to connect the electrodes to the patient, there is a need expert technicians. In addition, the system is not suitable for use at home. When considering all these disadvantages, practical system is needed to make sleep staging. In this study, Photoplethysmography signal use will be suggested for alternative to the signals used in sleep staging process. Photoplethysmography signal can measure through the skin of any part of the body with noninvasive method. In the study, the characteristic features of Photoplethysmography signals were analyzed whether it is distinctive for sleep and wake statistically by means of Mann-Whitney U Test. According to the results obtained p < 0.05 and all properties are meaningful for sleep-wake. All features can be used as distinctive for the sleep-wakefulness are considered and also a practical sleep staging system be realized using Photoplethysmography signal.Öğe Analysis of fetal heart rate signal based on neighborhood-based variance compression method(Ieee, 2018) Arıcan, Murat; Cömert, Zafer; Kocamaz, Adnan Fatih; Polat, KemalCardiotocography (CTG) is a fetal monitoring technique and it constitutes two distinct simultaneously recorded biophysical signals which are fetal heart rate (FHR) and uterine contractions (UC). In clinical practice, CTG traces are interpreted visually by obstetricians and midwives, and such a visual examination leads to an increase in disagreement level among observers. Although existing of several guidelines to ensure more consistent interpretation, computerized CTG analysis is seen as the most promising way to tackle the disadvantages which CTG has. In this study, we deal with a neighborhood-based variance compression method on FHR signals. For this particular purpose, we employed the proposed compression algorithms on normal and hypoxic samples obtained from an open-access intrapartum CTG database. The diagnostic indices obtained from time, frequency and bi-spectral domains were taken into account in the experiment. Also, the differences in original and compressed signal were examined statistically. The experimental results point out that the proposed algorithm can be used successfully for FHR signal compression.Öğe Application of the output dependent feature scaling in modeling and prediction of performance of counter flow vortex tube having various nozzles numbers at different inlet pressures of air, oxygen, nitrogen and argon(Elsevier Sci Ltd, 2011) Polat, Kemal; Kırmacı, VolkanIn this study, the performance of the counter flow type vortex tube with the input parameters including the nozzle number (N), the densities of inlet gases (air, oxygen, nitrogen, and argon) and the inlet pressure (P-inlet) has been modeled with the proposed hybrid method combining a novel data preprocessing called output dependent feature scaling (ODFS) and adaptive network based fuzzy inference system (ANFIS) by using the experimentally obtained data. In the developed system, output parameter temperature gradient between the cold and hot outlets has been determined using input parameters comprising (P-inlet), (N), and the density of gases. In order to evaluate the performance of hybrid method, the mean absolute error (MAE), mean square error (MSE), root mean square error (RMSE), determination coefficient (R-2), and Index of Agreement (IA) values have been used. The obtained results are 9.0670e-004 (MAE), 5.8563e-006 (MSE), 0.0024 (RMSE), 1.00 (R-2), and 1.00 (IA) using the hybrid method. (C) 2011 Elsevier Ltd and IIR. All rights reserved.Öğe Arrhythmia diagnosis from ECG signal pulses with one-dimensional convolutional neural networks(Elsevier, 2023) Senturk, Umit; Polat, Kemal; Yucedag, Ibrahim; Alenezi, FayadhA noninvasive technique, electrocardiography (ECG), is crucial in the detection and treatment of cardiovascular disorders. Periodic beats make up ECG signals, and these beats vary based on the internal dynamics of the cardiovascular system. It is highly challenging to categorize ECG beats that can be understood by specialists in the area. In recent years, attempts have been made to use artificial intelligence programs in conjunction with a database of specified ECG beats to identify autonomous cardiovascular illness. In this investigation, the PTB Diagnostic ECG Database and the MIT-BIH Arrhythmia Database were used to attempt to classify arrhythmias. A one-dimensional convolutional neural network (CNN, or ConvNet) model was used to estimate the arrhythmia classes. The ECG beats defined in the database are divided into five classes: normal (N), supraventricular premature (S), premature ventricular contraction (V), ventricular and normal fusion (F), and Unclassifiable beats (U). The utilized one-dimensional convolutional neural network (1D-CNN VGG16) model’s average accuracy in classifying arrhythmias was found to be 99.12%. With the aid of this study, a system of experts has been built to assist specialized doctors in the healthcare system. The high estimating success of the used model will help in combining the right diagnosis with the right therapy and saving lives. © 2023 Elsevier Inc. All rights reserved.Öğe ASHEED: Attention-shifting mechanism for depolarization of cluster head energy consumption in the smart sensing system(Pergamon-Elsevier Science Ltd, 2022) Lu, Xu; Chen, Kezhou; Liu, Jun; Chen, Rongjun; Wu, Wanqing; Polat, KemalThe sensing node clustering algorithm is a network topology control method that can effectively extend the lifetime of smart sensing systems (SSSMs). However, the traditional topology algorithms suffer from the excessively early death of cluster heads. Hence, the attention-shifting mechanism for energy consumption based on hybrid energy-efficient distributed clustering (HEED) is proposed in this paper, called attention-shifting hybrid energy-efficient distributed clustering (ASHEED). The energy consumption of the cluster heads are reduced by shifting data reception and fusion energy consumption of the cluster heads to other cluster member nodes (Agents) within its competitive radius. Agent selection is performed by communication energy consumption comparison to ensure the rationality of cluster heads and agent positions with the aim of reducing the communication energy consumption for re-clustering. Experiment results demonstrated that the proposed approach could maximize the balance of energy consumption of cluster heads and common nodes, maintain the integrity of the network, and extend the optimal operation time of SSSMs.Öğe Attention based CNN model for fire detection and localization in real-world images(PERGAMON-ELSEVIER SCIENCE LTD, 2022) Majid, Saima; Alenezi, Fayadh; Masood, Sarfaraz; Ahmad, Musheer; Gündüz, Emine Selda; Polat, KemalFire is a severe natural calamity that causes significant harm to human lives and the environment. Recent works have proposed the use of computer vision for developing a cost-effective automated fire detection system. This paper presents a custom framework for detecting fire using transfer learning with state-of-the-art CNNs trained over real-world fire breakout images. The framework also uses the Grad-CAM method for the visualization and localization of fire in the images. The model also uses an attention mechanism that has significantly assisted the network in achieving better performances. It was observed through Grad-CAM results that the proposed use of attention led the model towards better localization of fire in the images. Among the plethora of models explored, the EfficientNetB0 emerged as the best-suited network choice for the problem. For the selected real-world fire image dataset, a test accuracy of 95.40% strongly supports the model's efficiency in detecting fire from the presented image samples. Also, a very high recall of 97.61 highlights that the model has negligible false negatives, suggesting the network to be reliable for fire detection.Öğe Attention-based end-to-end CNN framework for content-based X-ray image retrieval(Tubitak Scientific & Technical Research Council Turkey, 2021) Öztürk, Şaban; Alhudhaif, Adi; Polat, KemalThe widespread use of medical imaging devices allows deep analysis of diseases. However, the task of examining medical images increases the burden of specialist doctors. Computer-assisted systems provide an effective management tool that enables these images to be analyzed automatically. Although these tools are used for various purposes, today, they are moving towards retrieval systems to access increasing data quickly. In hospitals, the need for content-based image retrieval systems is seriously evident in order to store all images effectively and access them quickly when necessary. In this study, an attention-based end-to-end convolutional neural network (CNN)framework that can provide effective access to similar images from a large X-ray dataset is presented. In the first part of the proposed framework, a fully convolutional network architecture with attention structures is presented. This section contains several layers for determining the saliency points of X-ray images. In the second part of the framework, the modified image with X-ray saliency map is converted to representative codes in Euclidean space by the ResNet-18 architecture. Finally, hash codes are obtained by transforming these codes into hamming spaces. The proposed study is superior in terms of high performance and customized layers compared to current state-of-the-art X-ray image retrieval methods in the literature. Extensive experimental studies reveal that the proposed framework can increase the current precision performance by up to 13Öğe Automated COVID-19 detection in chest X-ray images usingfine-tuned deep learning architectures(Wiley, 2022) Aggarwal, Sonam; Gupta, Sheifali; Alhudhaif, Adi; Koundal, Deepika; Gupta, Rupesh; Polat, KemalThe COVID-19 pandemic has a significant impact on human health globally. The illness is due to the presence of a virus manifesting itself in a widespread disease resulting in a high mortality rate in the whole world. According to the study, infected patients have distinct radiographic visual characteristics as well as dry cough, breathlessness, fever, and other symptoms. Although, the reverse transcription polymerase-chain reaction (RT-PCR) test has been used for COVID-19 testing its reliability is very low. Therefore, computed tomography and X-ray images have been widely used. Artificial intelligence coupled with X-ray technologies has recently shown to be more effective in the diagnosis of this disease. With this motivation, a comparative analysis of fine-tuned deep learning architectures has been made to speed up the detection and classification of COVID-19 patients from other pneumonia groups. The models used for this analysis are MobileNetV2, ResNet50, InceptionV3, NASNetMobile, VGG16, Xception, InceptionResNetV2 DenseNet121, which have been fine-tuned using a new set of layers replaced with the head of the network. This research work has carried out an analysis on two datasets. Dataset-1 includes the images of three classes: Normal, COVID, and Pneumonia. Dataset-2, in contrast, contains the same classes with more focus on two prominent pneumonia categories: bacterial pneumonia and viral pneumonia. The research was conducted on 959 X-ray images (250 of Bacterial Pneumonia, 250 of Viral Pneumonia, 209 of COVID, and 250 of Normal cases). Using the confusion matrix, the required results of different models have been computed. For the first dataset, DenseNet121 has obtained a 97% accuracy, while for the second dataset, MobileNetV2 has performed best with an accuracy of 81%.Öğe Automatic arrhythmia detection based on the probabilistic neural network with FPGA implementation(Hindawi Ltd, 2022) Srivastava, Rohini; Kumar, Basant; Alenezi, Fayadh; Alhudhaif, Adi; Althubiti, Sara A.; Polat, KemalThis paper presents a prototype implementation of arrhythmia classification using Probabilistic neural network (PNN). Arrhythmia is an irregular heartbeat, resulting in severe heart problems if not diagnosed early. Therefore, accurate and robust arrhythmia classification is a vital task for cardiac patients. The classification of ECG has been performed using PNN into eight ECG classes using a unique combination of six ECG features: heart rate, spectral entropy, and 4th order of autoregressive coefficients. In addition, FPGA implementation has been proposed to prototype the complete system of arrhythmia classification. Artix-7 board has been used for the FPGA implementation for easy and fast execution of the proposed arrhythmia classification. As a result, the average accuracy for ECG classification is found to be 98.27%, and the time consumed in the classification is found to be 17 seconds.Öğe Automatic classification of hypertension types based on personal features by machine learning algorithms(Hindawi Ltd, 2020) Nour, Majid; Polat, KemalHypertension (high blood pressure) is an important disease seen among the public, and early detection of hypertension is significant for early treatment. Hypertension is depicted as systolic blood pressure higher than 140 mmHg or diastolic blood pressure higher than 90 mmHg. In this paper, in order to detect the hypertension types based on the personal information and features, four machine learning (ML) methods including C4.5 decision tree classifier (DTC), random forest, linear discriminant analysis (LDA), and linear support vector machine (LSVM) have been used and then compared with each other. In the literature, we have first carried out the classification of hypertension types using classification algorithms based on personal data. To further explain the variability of the classifier type, four different classifier algorithms were selected for solving this problem. In the hypertension dataset, there are eight features including sex, age, height (cm), weight (kg), systolic blood pressure (mmHg), diastolic blood pressure (mmHg), heart rate (bpm), and BMI (kg/m(2)) to explain the hypertension status and then there are four classes comprising the normal (healthy), prehypertension, stage-1 hypertension, and stage-2 hypertension. In the classification of the hypertension dataset, the obtained classification accuracies are 99.5%, 99.5%, 96.3%, and 92.7% using the C4.5 decision tree classifier, random forest, LDA, and LSVM. The obtained results have shown that ML methods could be confidently used in the automatic determination of the hypertension types.Öğe Automatic classification of hypertension types based on personal features by machine learning algorithms (vol 2020, 2742781, 2020)(Hindawi Ltd, 2020) Nour, Majid; Polat, KemalCorrection/Düzeltme: In the article titled “Automatic Classification of Hypertension Types Based on Personal Features by Machine Learning Algorithms” [1], there was an error in the reference provided for the PPG-BP database, which can be correctly accessed atÖğe Automatic detection of hard exudates shadow region within retinal layers of OCT images(Hindawi Ltd, 2022) Singh, Maninder; Gupta, Vishal; Singh, Pramod Kumar; Gupta, Rajeev; Kumar, Basant; Polat, KemalThe optical coherence tomography (OCT) is useful in viewing cross-sectional retinal images and detecting various forms of retinal disorders from those images. Image processing methods and computational algorithms underlying this paper try to detect the shadowing region beneath exudates automatically. This paper presents a novel method for detecting hard exudates from retinal OCT images, often associated with macular edema near or within the outer plexiform layer. In this paper, an algorithm can automatically detect the presence of hard exudates in retinal OCT images, and these exudates appear as highly reflective spots. Still, they do not appear as noticeable bright spots because of their minute sizes in predevelopment phases. In the proposed work, we are using a method to detect the presence of hard exudates by analyzing their shadowing effect instead of focusing on brightness spots. The raster scanning operation is performed by traversing the retina horizontally, and noting up any change in normalized summation of brightness intensity (summing up the intensity from top to bottom retinal layers and normalized concerning retinal width) leads to the detection of minute as well as the presence for the detection of large exudates detection by differentiating this brightness intensity graph. The shadow region helps identify the hard exudates; in our proposed method, the output for three input images has been shown. There is an excellent agreement between the results generated by the proposed algorithm and the diagnostic opinion made by the ophthalmologist. The proposed method automatically detects the hard exudates using shadow regions, and it does not need any parameter settings or manual intervention. It can yield significant results by giving the position of shadow regions, which indicates the presence of exudates.Öğe Automatic detection of respiratory arrests in OSA patients using PPG and machine learning techniques(Springer, 2017) Uçar, Muhammed Kürşad; Bozkurt, Mehmet Recep; Bilgin, Cahit; Polat, KemalObstructive sleep apnea is a syndrome which is characterized by the decrease in air flow or respiratory arrest depending on upper respiratory tract obstructions recurring during sleep and often observed with the decrease in the oxygen saturation. The aim of this study was to determine the connection between the respiratory arrests and the photoplethysmography (PPG) signal in obstructive sleep apnea patients. Determination of this connection is important for the suggestion of using a new signal in diagnosis of the disease. Thirty-four time-domain features were extracted from the PPG signal in the study. The relation between these features and respiratory arrests was statistically investigated. The Mann-Whitney U test was applied to reveal whether this relation was incidental or statistically significant, and 32 out of 34 features were found statistically significant. After this stage, the features of the PPG signal were classified with k-nearest neighbors classification algorithm, radial basis function neural network, probabilistic neural network, multilayer feedforward neural network (MLFFNN) and ensemble classification method. The output of the classifiers was considered as apnea and control (normal). When the classifier results were compared, the best performance was obtained with MLFFNN. Test accuracy rate is 97.07 % and kappa value is 0.93 for MLFFNN. It has been concluded with the results obtained that respiratory arrests can be recognized through the PPG signal and the PPG signal can be used for the diagnosis of OSA.Öğe Automatic determination of digital modulation types with different noises using convolutional neural network based on time-frequency information(Elsevier, 2020) Daldal, Nihat; Cömert, Zafer; Polat, KemalIn this study, a novel digital modulation classification model has been proposed for automatically recognizing six different modulation types including amplitude shift keying (ASK), frequency shift keying (FSK), phase-shift keying (PSK), quadrate amplitude shift keying (QASK), quadrate frequency shift keying (QFSK), and quadrate phase-shift keying (QPSK). The determination of modulation type is significant in military communication, satellite communication systems, and submarine communication. To classify the modulation types, we have proposed a two-stage hybrid method combining short-time Fourier transform (STFT) and convolutional neural network (CNN). In the first stage, as the data source, the time-frequency information from these modulation signals have been extracted with STFT. This information has been obtained as 2D images to feed the input of the CNN deep learning method. In the second stage, the obtained 2D time-frequency information has been given to the input of the CNN algorithm to classify the modulation types. In this work, noises at various SNR values from 0 dB to 25 dB were created and added to the modulated signals. Even in the presence of noise, the proposed hybrid deep learning model achieved excellent results in the noised-modulation signals. (C) 2019 Elsevier B.V. All rights reserved.Öğe Automatic sleep staging in obstructive sleep apnea patients using photoplethysmography, heart rate variability signal and machine learning techniques(Springer, 2018) Uçar, Muhammed Kürşad; Bozkurt, Mehmet Recep; Bilgin, Cahit; Polat, KemalIt is extremely significant to identify sleep stages accurately in the diagnosis of obstructive sleep apnea. In the study, it was aimed at determining sleep and wakefulness using a practical and applicable method. For this purpose , the signal of heart rate variability (HRV) has been derived from photoplethysmography (PPG). Feature extraction has been made from PPG and HRV signals. Afterward, the features, which will represent sleep and wakefulness in the best possible way, have been selected using F-score feature selection method. The selected features were classified with k-nearest neighbors classification algorithm and support vector machines. According to the results of the classification, the classification accuracy rate was found to be 73.36 %, sensivity 0.81, and specificity 0.77. Examining the performance of the classification, classifier kappa value was obtained as 0.59, area under an receiver operating characteristic value as 0.79, tenfold cross-validation as 77.35 %, and F-measurement value as 0.79. According to the results accomplished, it was concluded that PPG and HRV signals could be used for sleep staging process. It is a great advantage that PPG signal can be measured more practically compared to the other sleep staging signals used in the literature. Improving the systems, in which these signals will be used, will make diagnosis methods more practical.Öğe Bispectral features and mean shift clustering for stress and emotion recognition from natural speech(Pergamon-Elsevier Science Ltd, 2017) C. K., Yogesh; Muthusamy, Hariharan; Rajamanickam, Yuvaraj; Ngadiran, Ruzelita; Adom, A. H.; Polat, KemalA new set of features and feature enhancement techniques are proposed to recognize emotion and stress from speech signal. The speech waveforms and the glottal waveforms (derived from the recorded emotional/stress speech waveforms) were processed by using third order statistics called bispectrum and 28 (14 from speech waveforms and 14 from glottal waveforms) bispectral based features. In this work, mean shift clustering was used to enhance the discrimination ability of the extracted Bispectral Features (BSFs). Four classifiers were used to distinguish different emotional and stressed states. The performance of the proposed method is tested with three databases. Different experiments were conducted and recognition rates were achieved in the range between 93.44% and 100% for Berlin emotional speech database (BES), between 73.81% and 97.23% for Surrey audio-visual expressed emotion database (SAVEE), between 93.8% and 100% for speech under simulated and actual stress simulated domain (SUSAS) (recognition of multi-style speech under stress-neutral, loud, lombard and anger) and 100% for SUSAS actual domain (recognition of three different levels of stress. high, medium and low). The obtained results indicate that the proposed bispectral based features and mean shift clustering provide promising results to recognize emotion and stress from speech signal. (C) 2017 Elsevier Ltd. All rights reserved.Öğe Brain tumor classification using the modified ResNet50 model based on transfer learning(Elsevier Sci LTD, 2023) Sharma, Arpit Kumar; Nandal, Amita; Dhaka, Arvind; Zhou, Liang; Alenezi, Fayadh; Polat, KemalBrain tumour classification is essential for determining the type and grade and deciding on therapy appropriately. Several diagnostic methods are used in the therapeutic therapy to identify brain tumours. MRI, on the other hand, offers superior picture clarity, which is why specialists depend on it. Furthermore, detecting cancer through the manual division of brain tumours is a time-consuming, exhausting, and difficult job. The handdesigned outlines for planned brain tumour growth methods are present in the majority of the instances. Segmentation is a highly reliable and precise method for assessing therapy prognosis, planning, and outcomes. Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) advancements have enabled us to investigate the illness with high precision in a short period of time. Such technologies have produced some remarkable results, particularly in the last twenty years. Such breakthroughs provide doctors with the ability to evaluate the human anatomy using high-resolution sections. The most recent approaches can improve diagnostic precision when examining patients using non-invasive means. This work introduces a brain tumour detection method. The model grows using ResNet50, feature extraction, and augmentation. CNN's pre-trained datasets are used to fine-tune transfer learning. The proposed design utilised elements of the ResNet50 model, removing the final layer and adding four additional layers to meet work conditions. This study uses the improved ResNet50 model to present a novel deep-learning approach based on a transfer learning technique for evaluating brain cancer categorisation accuracy. Performance metrics were used to evaluate the effectiveness of the proposed model, and the results were compared to those obtained using state-of-the-art methods.
