Advanced Hybrid Segmentation Model Leveraging AlexNet Architecture for Enhanced Liver Cancer Detection
Venkata Raja Sekhar Reddy Nagireddy, Khaja Shareef Shaik · Acadlore Transactions on AI and Machine Learning · 2023
Liver cancer, one of the rapidly escalating forms of cancer, remains a principal cause of mortality globally.Its death rates can be attenuated through vigilant monitoring and early detection.This study aims to develop a sophisticated model to assist medical professionals in the classification of liver tumours using biopsy tissue images, thereby facilitating preliminary diagnosis.The study presents a novel, bio-inspired deep learning strategy purposed for augmenting liver cancer detection.The uniqueness of this approach rests in its two-fold contribution: Firstly, an innovative hybrid segmentation technique, integrating the SegNet network, UNet network, and Al-Biruni Earth Radius (BER) procedure, is introduced to extract liver lesions from Computed Tomography (CT) images.The algorithm initially applies the SegNet to isolate the liver from the abdominal image in a CT scan.Since hyperparameters significantly influence segmentation performance, the BER algorithm is hybridized with each network for optimal tuning.The method proposed herein is inspired by the pursuit of a common objective by swarm members.Al-Biruni's methodology for calculating Earth's radius sets the search space, extending beyond local solutions that require exploration.Secondly, a pre-trained AlexNet model is utilized for diagnosis, further enhancing the method's effectiveness.The proposed segmentation and classification algorithms have been compared with contemporary state-of-the-art techniques.The results demonstrated that in terms of specificity, F1-score, accuracy, and computational time, the proposed method outperforms its competitors, indicating its potential in advancing liver cancer detection.