Classification and Optimization of Liver Cancer Detection using Capsule-Vectored Neural Network with Artificial Jelly Optimization

Liver cancer diagnostic procedures currently use CT together with MRI and ultrasonic scans to check for tumors while several diagnostic challenges like tissue superimposition and irregular tumor formation limit test precision. The proposed solution involves deploying CV2N-AJOpt which stands for Capsule-Vectored Neural Network with Artificial Jelly Optimization. The LiTS dataset receives preprocess treatment using Gradient Domain Guided Filtering (2GDF) to improve image quality and remove noise. STFT-CV2Net serves as the combination of Short-Time Fourier Transform and Capsule-Vectored Neural Network to execute feature extraction and classification operations. AJO performs weight optimization of CV2Net to enhance its operational performance. The proposed model delivers experimental results showing 99.8% recall and 99.9% accuracy which establishes superiority compared to existing methods. CV2N-AJOpt shows excellence at reducing wrong detections while developing high accuracy in detecting complex liver cancer anatomical structures. STFT-CV2Net demonstrates value as a clinical diagnosis tool for liver cancer because it effectively processes extensive medical data while performing with reliability.