Background: Traditional endoscopy is an invasive and painful method of examining the gastrointestinal tract (GIT) not supported by physicians and patients. To handle this issue, video endoscopy (VE) or wireless capsule endoscopy (WCE) is recommended and utilized for GIT examination. Furthermore, manual assessment of captured images is not possible for an expert physician because it’s a time taking task to analyze thousands of images thoroughly. Hence, there comes the need for a Computer-Aided-Diagnosis (CAD) method to help doctors analyze images. Many researchers have proposed techniques for automated recognition and classification of abnormality in captured images. Methods: In this article, existing methods for automated classification, segmentation and detection of several GI diseases are discussed. Paper gives a comprehensive detail about these state-of-theart methods. Furthermore, literature is divided into several subsections based on preprocessing techniques, segmentation techniques, handcrafted features based techniques and deep learning based techniques. Finally, issues, challenges and limitations are also undertaken. Results: A comparative analysis of different approaches for the detection and classification of GI infections. Conclusion: This comprehensive review article combines information related to a number of GI diseases diagnosis methods at one place. This article will facilitate the researchers to develop new algorithms and approaches for early detection of GI diseases detection with more promising results as compared to the existing ones of literature.

Esophagitis, cancerous growths, bleeding, and ulcers are typical symptoms of gastrointestinal disorders, which account for a significant portion of human mortality. For both patients and doctors, traditional diagnostic methods can be exhausting. The major aim of this research is to propose a hybrid method that can accurately diagnose the gastrointestinal tract abnormalities and promote early treatment that will be helpful in reducing the death cases. The major phases of the proposed method are: Dataset Augmentation, Preprocessing, Features Engineering (Features Extraction, Fusion, Optimization), and Classification. Image enhancement is performed using hybrid contrast stretching algorithms. Deep Learning features are extracted through transfer learning from the ResNet18 model and the proposed XcepNet23 model. The obtained deep features are ensembled with the texture features. The ensemble feature vector is optimized using the Binary Dragonfly algorithm (BDA), Moth–Flame Optimization (MFO) algorithm, and Particle Swarm Optimization (PSO) algorithm. In this research, two datasets (Hybrid dataset and Kvasir-V1 dataset) consisting of five and eight classes, respectively, are utilized. Compared to the most recent methods, the accuracy achieved by the proposed method on both datasets was superior. The Q_SVM’s accuracies on the Hybrid dataset, which was 100%, and the Kvasir-V1 dataset, which was 99.24%, were both promising.

An automated system is proposed for the detection and classification of GI abnormalities. The proposed method operates under two pipeline procedures: (a) segmentation of the bleeding infection region and (b) classification of GI abnormalities by deep learning. The first bleeding region is segmented using a hybrid approach. The threshold is applied to each channel extracted from the original RGB image. Later, all channels are merged through mutual information and pixel-based techniques. As a result, the image is segmented. Texture and deep learning features are extracted in the proposed classification task. The transfer learning (TL) approach is used for the extraction of deep features. The Local Binary Pattern (LBP) method is used for texture features. Later, an entropy-based feature selection approach is implemented to select the best features of both deep learning and texture vectors. The selected optimal features are combined with a serial-based technique and the resulting vector is fed to the Ensemble Learning Classifier. The experimental process is evaluated on the basis of two datasets: Private and KVASIR. The accuracy achieved is 99.8 per cent for the private data set and 86.4 percent for the KVASIR data set. It can be confirmed that the proposed method is effective in detecting and classifying GI abnormalities and exceeds other methods of comparison.

In recent years breast cancer detection has been the most popular research topic in medical image analysis. It is the most common malignancy in women, and men can also be affected. Conferring to the American Cancer Society, in 2019, almost two million new cases were registered, and the death rate was almost 41,000. The death rate can be reduced if the cancer is timely diagnosed. For cancer detection, different modalities are used, like MRI, ultrasound, and mammography. The most common and popular modality is mammography. A mammogram shows breast irregularities that are benign or malignant. In digital mammography, it is not easy to extract accurate breast regions. The main problem in the extraction region of concern is pectoral muscle suppression. The pectoral muscle appears in the breast area. Sometimes it is marked as an area of attention that causes a false positive rate. It is essential to eradicate pectoral muscles from the breast. This manuscript overviews the introduction of basic breast cancer terminologies. The work also analyzes state-of-the-insight imaging procedures used for breast cancer analysis.