Computer Aided Diagnosis for Gastrointestinal Cancer Classification using Hybrid Rice Optimization with Deep Learning

A gastrointestinal disease is a group of cancers which mainly affects the digestive system, along with stomach, small intestine, oesophagus, rectum, and colon. Accurate classification earlier diagnosis this cancer are crucial for better patient outcomes. Deep learning (DL) algorithm, especially convolutional neural network (CNN), trained to categorize endoscopic images tissue as either benign or malignant. Gastrointestinal (GC) DL process using artificial intelligence (AI), gastric It could help clinicians identify earliest symptoms make treatment decisions, resulting in improved The study designs new Detection Classification Hybrid Rice Optimization Learning (GDDC-HRODL) model. presented GDDC-HRODL model intends classify medical GC. To achieve this, technique initially preprocesses input data improve image quality. In addition, algorithm employs HybridNet produce feature vectors hyperparameter tuning takes place HRO algorithm. For GC purposes, uses an attention-based long short-term memory (ALSTM) its hyperparameters can be selected by ant lion optimization (ALO) design processes helps accomplish enhanced performance. experimental analysis on dataset demonstrates betterment process.