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    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, Kemal
    Brain 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.
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    Brain tumor detection with multi-scale fractal feature network and fractal residual learning
    (Elsevier, 2024) Jakhar, Shyo Prakash; Nandal, Amita; Dhaka, Arvind; Alhudhaif, Adi; Polat, Kemal
    Deep learning has enabled the creation of several approaches for segmenting brain tumors using convolutional neural networks. These methods have come about as a direct result of the advancement of the field of machine learning. The proposed pixel-level segmentation is based on fractal residual deep learning; provide an insufficient degree of sensitivity when used for tumor segmentation. This is achieved due to fractal feature extraction and multi-scale approach used for segmentation. If multi-level segmentation is used, it is possible to effectively increase the sensitivity of the segmentation process which is the additional benefit from the proposed method. In this work, the production of tumor region is based on multi-scale pixel segmentation. This approach protects the integrity of tumor information while simultaneously improving the detection accuracy by cutting down on the total number of tumor regions. When compared to the information about the brain found in tumor locations, the proposed strategy has the potential to enhance the percentage of brain tumor information. This work proposes a novel network structure known as the Mutli-scale fractal feature network (MFFN) to increase the accuracy of the network's classification as well as its sensitivity when it comes to the segmentation of brain tumors. The proposed method with overall feature results in 94.66% accuracy, 94.42% sensitivity and 92.81% specificity using 5fold cross validation. In this paper the Cancer Imaging Archive (TCIA) dataset has been used in order to evaluate performance evaluation metrics and segmentation results to quantify the superiority of proposed brain tumor detection approach in comparison to existing methods.
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    Fusion of overexposed and underexposed images using caputo differential operator for resolution and texture based enhancement
    (Springer, 2023) Zhou, Liang; Alenezi, Fayadh S.; Nandal, Amita; Dhaka, Arvind; Wu, Tao; Polat, Kemal
    The visual quality of images captured under sub-optimal lighting conditions, such as over and underexposure may benefit from improvement using fusion-based techniques. This paper presents the Caputo Differential Operator-based image fusion technique for image enhancement. To effect this enhancement, the proposed algorithm first decomposes the overexposed and underexposed images into horizontal and vertical sub-bands using Discrete Wavelet Transform (DWT). The horizontal and vertical sub-bands are then enhanced using Caputo Differential Operator (CDO) and fused by taking the average of the transformed horizontal and vertical fractional derivatives. This work introduces a fractional derivative-based edge and feature enhancement to be used in conjuction with DWT and inverse DWT (IDWT) operations. The proposed algorithm combines the salient features of overexposed and underexposed images and enhances the fused image effectively. We use the fractional derivative-based method because it restores the edge and texture information more efficiently than existing method. In addition, we have introduced a resolution enhancement operator to correct and balance the overexposed and underexposed images, together with the Caputo enhanced fused image we obtain an image with significantly deepened resolution. Finally, we introduce a novel texture enhancing and smoothing operation to yield the final image. We apply subjective and objective evaluations of the proposed algorithm in direct comparison with other existing image fusion methods. Our approach results in aesthetically subjective image enhancement, and objectively measured improvement metrics.
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    Fusion of overexposed and underexposed images using caputo differential operator for resolution and texture based enhancement (nov, 10.1007/s10489-022-04344-z, 2022)
    (Springer, 2023) Zhou, Liang; Alenezi, Fayadh S. S.; Nandal, Amita; Dhaka, Arvind; Wu, Tao; Polat, Kemal
    The article Fusion of overexposed and underexposed images using caputo differential operator for resolution and texture based enhancement, written by Liang Zhou, Fayadh S. Alenezi, Amita Nandal, Arvind Dhaka, Tao Wu, Deepika Koundal, Adi Alhudhaif and Kemal Polat, was originally published electronically on the publisher’s internet portal on November 29, 2022 without open access.
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    HOG transformation based feature extraction framework in modified Resnet50 model for brain tumor detection
    (Elsevier Science Ltd, 2023) Sharma, Arpit Kumar; Nandal, Amita; Dhaka, Arvind; Polat, Kemal; Alwadie, Raghad; Alenezi, Fayadh
    Brain tumor happens due to the instant and uncontrolled cell growth. It may lead to death if not cured at an early stage. In spite of several promising results and substantial efforts in this research area, the real challenge is to provide the accurate classification and segmentation. The key issue in brain tumor detection develops from the irregular changes in the tumor size, shape and location. In assessing the MRI images, computer-aided diagnoses are playing an extraordinary role and can help clinicians/radiologist. Nowadays, brain tumor has become the most incursive ailment that leads to a very short life expectancy when it reaches its highest grade. This research paper has created a new model using histogram of gradient (HOG) based neural features from MRI images for tumors detection. This research has conducted the feature optimization approach to achieve additional instinctive features from the complex feature vector. We developed a Modified ResNet50 model with HOG technique. The modified ResNet50 model can accurately extract the deep feature using deep learning approach. This model is applied along with the upgraded layered architecture in order to keep the optimal computational efficiency. We have also used the augmentation and feature extraction techniques using machine learning-based ensemble classifier that further provides the optimized fusion vector to identify the tumor. Such hybrid approach provides excellent performance with the detection accuracy of 88% with HOG and modified ResNet50. The results are also compared with the recent state of art methods.
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    Spectrally distinct pixel extraction and kernel filtering for brain tumour diagnosis
    (Elsevier Sci Ltd, 2024) Alhudhaif, Adi; Alsubai, Shtwai; Aseeri, Ahmad O.; Nandal, Amita; Polat, Kemal
    Before surgery, medical segmentation is carried out to ascertain the limits of regions of interest (ROI). Critical information may be gathered during the planning phase of the operation if it is permitted that the growth, structure, and behaviour of the ROI be studied. This will increase the probability that the operation will be successful. Most of the time, segmentations are carried out either manually or via machine learning algorithms based on hand annotations. The difference between pure background information and an approximation of such information would affect the performance of brain tumour detection algorithms. This paper offers a technique for the identification of tumour regions that is based on kernel background filtration. The proposed approach presented has a more precise estimation of background information as its primary objective. It is composed of four stages. The first stage includes a probabilistic method based on image denoising. The second stage includes extracting the pure background pixels using a kernel-based technique. The third stage estimates the background covariance matrix using the pixels taken from the pure background pixels. In the fourth phase, we perform segmentation using DCNN. The segmentation procedure begins with training a deep convolutional neural network (DCNN) to recreate damaged brain regions. Window-based methods identify pixels with the largest reconstruction loss. This study isolates tumorous regions using pixel-wise segmentation. The proposed method segments tumours of various sizes with an average Dice score of 0.82.