Diabetic Retinopathy (DR) is a common eye disease worldwide and usually found in diabetes patients. Deep learning based algorithms have recently produced promising findings for the automatic detection and segmentation of DR lesions from fundus images. In this paper, we present an approach for segmenting DR using a Residual U-Net model. Here, we have incorporated Squeeze and Excite (SE) blocks into the Residual U-Net architecture to segment various DR diseases from fundus images. SE blocks are attention techniques that dynamically recalibrate the feature maps of a neural network. These enable the network to prioritize useful features while suppressing less important ones, hence boosting the model’s ability to discriminate. We intended to enhance the segmentation performance by enabling the model to better capture spatial and contextual information by integrating SE blocks into the Residual U-Net architecture. The developed model architecture was trained and tested on fundus image DR datasets. The results obtained by the developed model for hard exudates and hemorrhages segmentations were better as compared to U-Net and its variants.

Breast cancer is a disease where breast cells grow out of control and leads to cancer. Various methodologies have been developed to identify breast cancer. In this paper, we have developed an approach to classify breast cancer from histopathology images. The approach makes use of deep learning based architectures by setting same parameters for all while training and testing on them. Thereafter, all the architectures are compared to see which one is most suited for the classification of breast cancer. Previous works on AlexNet, VGG, ResNet have already been published, and here we have tried to see the performance of those models which have less number of trainable parameters, namely DenseNet121, DenseNet169, DenseNet201, EfficientNetB0, EfficientNetB5, EfficientNetV2B0 and EfficientNetV2S. Here, all the experiments are conducted on BreakHis histopathology dataset by utilizing all the images of resolutions 40X, 100X, 200X and 400X of benign and malignant cancer.

Abstract Skin cancer is the most well‐known disease found in the individuals who are exposed to the Sun's ultra‐violet (UV) radiations. It is identified when skin tissues on the epidermis grow in an uncontrolled manner and appears to be of different colour than the normal skin tissues. This paper focuses on predicting the class of dermascopic images as benign and malignant. A new feature extraction method has been proposed to carry out this work which can extract relevant features from image texture. Local and gradient information from and directions of images has been utilized for feature extraction. After that images are classified using machine learning algorithms by using those extracted features. The efficacy of the proposed feature extraction method has been proved by conducting several experiments on the publicly available image dataset 2016 International Skin Imaging Collaboration (ISIC 2016). The classification results obtained by the method are also compared with state‐of‐the‐art feature extraction methods which show that it performs better than others. The evaluation criteria used to obtain the results are accuracy, true positive rate (TPR) and false positive rate (FPR) where TPR and FPR are used for generating receiver operating characteristic curves.

Melanoma is a type of skin cancer that is mainly caused by intense UV exposure. If melanoma is identified at an early stage, then it is generally remediable. However, if it is not diagnosed properly, cancer can grow to rest of the body which then makes it difficult to cure and can be lethal. Conventionally, melanoma is diagnosed through visual methods and biopsies but their accuracy may not be reliable for all the cases. Hence, the risks involved for such a diagnosis have emerged identification and classification of melanoma as benign or malignant a very important research problem in medical imaging. This paper employs various feature descriptors like local binary pattern (LBP), complete LBP (CLBP) and their variants, which are based on histogram mapping such as uniform, rotation invariant and rotation invariant uniform patterns. The extracted features are then used to train different classifiers such as decision tree, random forest (RF), support vector machine (SVM) and k nearest neighbour (kNN). A comparative study of the various feature descriptors and classifiers are analyzed for accurate identification and classification of melanoma as benign or malignant. An image dataset which has been used in our work has been downloaded from ISIC-Archive, which consists of 947 dermoscopic images and the dataset is made freely available online by realizing the importance of the research. The best accuracy has been obtained by using RF in CLBP with an accuracy of 80.3%.