Segmentation and Classification of Skin Cancer in Dermoscopy Images Using SAM-Based Deep Belief Networks
Journal Title: Healthcraft Frontiers - Year 2023, Vol 1, Issue 1
Abstract
In the field of computer-aided diagnostics, the segmentation and classification of biomedical images play a pivotal role. This study introduces a novel approach employing a Self-Augmented Multistage Deep Learning Network (SAMNetwork) and Deep Belief Networks (DBNs) optimized by Coot Optimization Algorithms (COAs) for the analysis of dermoscopy images. The unique challenges posed by dermoscopy images, including complex detection backgrounds and lesion characteristics, necessitate advanced techniques for accurate lesion recognition. Traditional methods have predominantly focused on utilizing larger, more complex models to increase detection accuracy, yet have often neglected the significant intraclass variability and inter-class similarity of lesion traits. This oversight has led to challenges in algorithmic application to larger models. The current research addresses these limitations by leveraging SAM, which, although not yielding immediate high-quality segmentation for medical image data, provides valuable masks, features, and stability scores for developing and training enhanced medical images. Subsequently, DBNs, aided by COAs to fine-tune their hyper-parameters, perform the classification task. The effectiveness of this methodology was assessed through comprehensive experimental comparisons and feature visualization analyses. The results demonstrated the superiority of the proposed approach over the current state-of-the-art deep learning-based methods across three datasets: ISBI 2017, ISBI 2018, and the PH2 dataset. In the experimental evaluations, the Multi-class Dilated D-Net (MD2N) model achieved a Matthew’s Correlation Coefficient (MCC) of 0.86201, the Deep convolutional neural networks (DCNN) model 0.84111, the standalone DBN 0.91157, the autoencoder (AE) model 0.88662, and the DBN-COA model 0.93291, respectively. These findings highlight the enhanced performance and potential of integrating SAM with optimized DBNs in the detection and classification of skin cancer in dermoscopy images, marking a significant advancement in the field of medical image analysis.
Authors and Affiliations
Syed Ziaur Rahman, Tejesh Reddy Singasani, Khaja Shareef Shaik
Keywords
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- EP ID EP732235
- DOI https://doi.org/10.56578/hf010102
- Views 95
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