Implementing the Hybrid Segmentation with Adaptive Densenet and Improved Heuristic Approach for Classifying the Prostate Cancer
Keywords:
Prostate Cancer Classification; Prostate Image; TransUnet and Segnet; Dilated Hybrid Segmentation; Adaptive and Attentive Multiscale Densenet; Enhanced Capuchin Search Algorithm.Abstract
Early detection of prostate cancer is challenging due to its subtle symptoms, which has led to the exploration of deep learning algorithms to improve diagnostic accuracy of MRI images. In response to these challenges, the development of a computer-aided detection (CAD) system with segmentation and categorization techniques has become increasingly important. In this research, a novel hybrid prostate cancer detection method that utilizes segmentation and adaptive model classification is proposed to overcome the current limitations. The proposed method includes three main phases: Image acquisition, segmentation and classification. At the beginning, images are extracted from publicly available online databases. Then, these images are subjected to Dilated Hybrid Segmentation (DHS), which integrates TransUnet with Segnet to accurately delineate prostate lesions. Finally, the segmented images are fed into the Adaptive and Attentive Multiscale Densenet (AAMDNet) classification model, where certain hyper parameters are optimized using the Enhanced Capuchin Search Algorithm (ECapSA). The performance of the model is then evaluated using various metrics. Compared to conventional approaches, this novel system delivers impressive results, and shows higher accuracy in the classification of prostate cancer.
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