Wireless Capsule Endoscopy Infected Images Detection and Classification Using MobileNetV2-BiLSTM Model.

An efficient tool to execute painless imaging and examine gastrointestinal tract illnesses of the intestine is also known as wireless capsule endoscopy (WCE). Performance, safety, tolerance, and efficacy are the several concerns that make adaptation challenging and wide applicability. In addition, to detect abnormalities, the great importance is the automatic analysis of the WCE dataset. These issues are resolved by numerous vision-based and computer-aided solutions. But, they want further enhancements and do not give the accuracy at the desired level. In order to solve these issues, this paper presents the detection and classification of WCE infected images by a deep neural network and utilizes a bleed image recognizer (BIR) that associates the MobileNetV2 design to classify the images of WCE infected. For the opening-level evaluation, the BIR uses the MobileNetV2 model for its minimum computation power necessity, and then the outcome is sent to the CNN for more processing. Then, Bi-LSTM with an attention mechanism is used to improve the performance level of the model. Hybrid attention Bi-LSTM design yields more accurate classification outcomes. The proposed scheme is implemented in the Python platform and the performance is evaluated by Cohen's kappa, F1-score, recall, accuracy, and precision. The implementation outcomes show that the introduced scheme achieved maximum accuracy of 0.996 with data augmentation with the dataset of WCE images which provided higher outcomes than the others. [ABSTRACT FROM AUTHOR]