Endoscopic Image Analysis for Gastrointestinal Tract Disease Diagnosis Using Nature Inspired Algorithm With Deep Learning Approach

Endoscopic image analysis has played a pivotal function in the diagnosis and management of gastrointestinal (GI) tract diseases. Gastrointestinal endoscopy is a medical procedure where a flexible tube with an endoscope (camera) is inserted into the GI tract to visualize the inner lining of the colon, esophagus, stomach, and small intestine. The videos and images attained during endoscopy provide valuable data for detecting and monitoring a large number of GI diseases. Computer-assisted automated diagnosis technique helps to achieve accurate diagnoses and provide the patient the relevant medical care. Machine learning (ML) and deep learning (DL) methods have been exploited to endoscopic images for classifying diseases and providing diagnostic support. Convolutional Neural Networks (CNN) and other DL algorithms can learn to discriminate between various kinds of GI lesions based on visual properties. This study presents an Endoscopic Image Analysis for Gastrointestinal Tract Disease Diagnosis using an inspired Algorithm with Deep Learning (EIAGTD-NIADL) technique. The EIAGTD-NIADL technique intends to examine the endoscopic images using nature nature-inspired algorithm with a DL model for gastrointestinal tract disease detection and classification. To pre-process the input endoscopic images, the EIAGTD-NIADL technique uses a bilateral filtering (BF) approach. For feature extraction, the EIAGTD-NIADL technique applies an improved ShuffleNet model. To improve the efficacy of the improved ShuffleNet model, the EIAGTD-NIADL technique uses an improved spotted hyena optimizer (ISHO) algorithm. Finally, the classification process is performed by the use of the stacked long short-term memory (SLSTM) method. The experimental outcomes of the EIAGTD-NIADL system can be confirmed on benchmark medical image datasets. The obtained outcomes demonstrate the promising results of the EIAGTD-NIADL approach over other models

Computer-Assisted Classification Patterns in Autoimmune Diagnostics: The AIDA Project

Antinuclear antibodies (ANAs) are significant biomarkers in the diagnosis of autoimmune diseases in humans, done by mean of Indirect ImmunoFluorescence (IIF) method, and performed by analyzing patterns and fluorescence intensity. This paper introduces the AIDA Project (autoimmunity: diagnosis assisted by computer) developed in the framework of an Italy-Tunisia cross-border cooperation and its preliminary results. A database of interpreted IIF images is being collected through the exchange of images and double reporting and a Gold Standard database, containing around 1000 double reported images, has been settled. The Gold Standard database is used for optimization of a CAD (Computer Aided Detection) solution and for the assessment of its added value, in order to be applied along with an Immunologist as a second Reader in detection of autoantibodies. This CAD system is able to identify on IIF images the fluorescence intensity and the fluorescence pattern. Preliminary results show that CAD, used as second Reader, appeared to perform better than Junior Immunologists and hence may significantly improve their efficacy; compared with two Junior Immunologists, the CAD system showed higher Intensity Accuracy (85,5% versus 66,0% and 66,0%), higher Patterns Accuracy (79,3% versus 48,0% and 66,2%), and higher Mean Class Accuracy (79,4% versus 56,7% and 64.2%)