Yazar "Armghan, Ammar" seçeneğine göre listele

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    An effective hashing method using W-Shaped contrastive loss for imbalanced datasets
    (Pergamon-Elsevier Science Ltd, 2022) Alenezi, Fayadh; Öztürk, Şaban; Armghan, Ammar; Polat, Kemal
    The extraction of informative features from medical images and the retrieving of similar images from data repositories is vital for clinical decision support systems. Unlike general tasks such as medical image classification and segmentation, retrieval is more reliable in terms of interpretability. However, this task is quite challenging due to the multimodal and imbalanced nature of medical images. Because traditional retrieval methods use hand-crafted feature extraction guided approximate hashing functions, they often have problems capturing the latent characteristics of images. Deep learning based retrieval methods can eliminate drawbacks of hand-crafted feature extraction methods. However, in order for a deep architecture to produce high performance, large-scale datasets containing labeled and balanced samples are required. Since most medical datasets do not have these properties, existing hashing methods are not powerful enough to model patterns in medical images, which have a similar general appearance but subtle differences. In this study, a novel W-shaped contrastive loss (W-SCL) is proposed for skin lesion image retrieval on a dataset whose visual difference between classes is relatively low. We considerably improve the traditional contrastive loss (CL) performance by including label information for very similar skin lesion images. We use two benchmark datasets consisting of general images and two benchmark skin lesion datasets to test the proposed W-SCL performance. In addition, experiments are carried out using various pre-trained CNN and shallow CNN architectures. These extensive experiments reveal that the proposed method improves the mean average precision (mAP) performance by approximately 7% for general image datasets and approximately 12% for skin lesion datasets.
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    A multi-stage melanoma recognition framework with deep residual neural network and hyperparameter optimization-based decision support in dermoscopy images
    (Pergamon-Elsevier Science Ltd, 2023) Alenezi, Fayadh; Armghan, Ammar; Polat, Kemal
    This paper developed a novel melanoma diagnosis model from dermoscopy images using a novel hybrid model. Melanoma is the most dangerous and rarest type of skin cancer. It is seen because of the uncontrolled prolif-eration of melanocyte cells that give color to the skin. Dermoscopy is a critical auxiliary diagnostic method in the differentiation of pigmented moles, which show moles by magnifying 10-20 times from skin cancers. This paper proposes a multi-stage melanoma recognition framework with skin lesion images obtained from dermoscopy. This model developed a practical pre-processing approach that includes dilation and pooling layers to remove hair details and reveal details in dermoscopy images. A deep residual neural network was then utilized as the feature extractor for processed images.Additionally, the Relief algorithm selected practical and distinctive features from these features. Finally, these selected features were fed to the input of the support vector machine (SVM) classifier. In addition, the Bayesian optimization algorithm was used for the optimum parameter selection of the SVM method. The International Skin Imaging Collaboration (ISIC-2019 and ISIC-2020) datasets were used to test the performance of the pro-posed model. As a result, the proposed model produced approximately 99% accuracy for classifying melanoma or benign from skin lesion images. These results show that the proposed model can help physicians to automatically identify melanoma based on dermatological imaging.
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    A novel multi-task learning network based on melanoma segmentation and classification with skin lesion images
    (MDPI, 2023) Alenezi, Fayadh; Armghan, Ammar; Polat, Kemal
    Melanoma is known worldwide as a malignant tumor and the fastest-growing skin cancer type. It is a very life-threatening disease with a high mortality rate. Automatic melanoma detection improves the early detection of the disease and the survival rate. In accordance with this purpose, we presented a multi-task learning approach based on melanoma recognition with dermoscopy images. Firstly, an effective pre-processing approach based on max pooling, contrast, and shape filters is used to eliminate hair details and to perform image enhancement operations. Next, the lesion region was segmented with a VGGNet model-based FCN Layer architecture using enhanced images. Later, a cropping process was performed for the detected lesions. Then, the cropped images were converted to the input size of the classifier model using the very deep super-resolution neural network approach, and the decrease in image resolution was minimized. Finally, a deep learning network approach based on pre-trained convolutional neural networks was developed for melanoma classification. We used the International Skin Imaging Collaboration, a publicly available dermoscopic skin lesion dataset in experimental studies. While the performance measures of accuracy, specificity, precision, and sensitivity, obtained for segmentation of the lesion region, were produced at rates of 96.99%, 92.53%, 97.65%, and 98.41%, respectively, the performance measures achieved rates for classification of 97.73%, 99.83%, 99.83%, and 95.67%, respectively.
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    Wavelet transform based deep residual neural network and ReLU based Extreme Learning Machine for skin lesion classification
    (Pergamon-Elsevier Science Ltd, 2023) Alenezi, Fayadh; Armghan, Ammar; Polat, Kemal
    Skin cancer is one of the most widespread threats to human health worldwide. Therefore, early-stage recognition and detection of these diseases are crucial for patients' lives. Computer-aided methods can be used to solve this problem with high performance. We presented a wavelet transform-based deep residual neural network (WT-DRNNet) for skin lesion classification. In the proposed model, wavelet transformation, pooling, and normali-zation reveal more refined details and eliminate unwanted details from skin lesion images. Then, deep features are extracted with the residual neural network based on transfer learning as a feature extractor. Finally, these deep features were combined with the global average pooling approach, and the training phase was carried out using the Extreme Learning Machine based on the ReLu activation function. The ISIC2017 and HAM10000 datasets were used in the experimental works to test the proposed model's performance. The performance metrics of accuracy, specificity, precision, and F1-Score of the proposed model for the ISIC2017 dataset were 96.91%, 97.68%, 96.43%, and 95.79%, respectively, while these metrics for the HAM10000 dataset were 95.73%, 98.8%, 95.84%, and 93.44%, respectively. These results outperform the state-of-the-art to classify skin lesions. As a result, the proposed model can assist specialist physicians in automatically classifying cancer-based on skin lesion images