Convolutional neural networks (CNN) have become a popular choice for image segmentation and classification. Internal body images are obscure in nature with involvement of noise, luminance variation, rotation and blur. Thus optimal choice of features for machine learning model to classify bleeding is still an open problem. CNN is efficient for attribute selection and ensemble learning makes a generalized robust system. Capsule endoscopy is a new technology which enables a gastroenterologist to visualize the entire digestive tract including small bowel to diagnose bleeding, ulcer and polyp. This paper presents a supervised learning ensemble to detect the bleeding in the images of Wireless Capsule Endoscopy. It accurately finds out the best possible combination of attributes required to classify bleeding symptoms in endoscopy images. A careful setting for CNN layer options and optimizer for back propagation after reducing the color palette using minimum variance quantization has shown promising results. Results of testing on public and real dataset has been analyzed. Proposed ensemble is able to achieve 0.95 on the public endoscopy dataset and 0.93 accuracy on the real video dataset. A detailed data analysis has also been incorporated in the study including RGB pixel intensities, distributions of binary classes and various class ratios for training.

With the evolution of modern digital pathology, examining cancer cell tissues has paved the way to quantify subtle symptoms, for example, by means of image staining procedures using Eosin and Hematoxylin. Cancer tissues in the case of breast and lung cancer are quite challenging to examine by manual expert analysis of patients suffering from cancer. Merely relying on the observable characteristics by histopathologists for cell profiling may under-constrain the scale and diagnostic quality due to tedious repetition with constant concentration. Thus, automatic analysis of cancer cells has been proposed with algorithmic and soft-computing techniques to leverage speed and reliability. The paper’s novelty lies in the utility of Zernike image moments to extract complex features from cancer cell images and using simple neural networks for classification, followed by explainability on the test results using the Local Interpretable Model-Agnostic Explanations (LIME) technique and Explainable Artificial Intelligence (XAI). The general workflow of the proposed high throughput strategy involves acquiring the BreakHis public dataset, which consists of microscopic images, followed by the application of image processing and machine learning techniques. The recommended technique has been mathematically substantiated and compared with the state-of-the-art to justify the empirical basis in the pursuit of our algorithmic discovery. The proposed system is able to classify malignant and benign cancer cell images of 40× resolution with 100% recognition rate. XAI interprets and reasons the test results obtained from the machine learning model, making it reliable and transparent for analysis and parameter tuning.