Advanced Hybrid Segmentation Model Leveraging AlexNet Architecture for Enhanced Liver Cancer Detection
Journal Title: Acadlore Transactions on AI and Machine Learning - Year 2023, Vol 2, Issue 3
Abstract
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.
Authors and Affiliations
Venkata Raja Sekhar Reddy Nagireddy,Khaja Shareef Shaik
Keywords
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- EP ID EP731889
- DOI https://doi.org/10.56578/ataiml020301
- Views 80
- Downloads 1
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