An Efficient Skin Cancer Segmentation and Classification with Severity Analysis Using HAM10000
DOI:
https://doi.org/10.52783/jns.v14.3050Abstract
The skin is the largest organ in the human body. The most common form of cancer that poses a significant threat to public health is skin cancer with melanoma being particularly deadly. The successful treatment depends on early advancement, yet traditional diagnostic methods often fail due to limitations in image quality and the complexity of visual differentiation. This study explores and propose an efficient deep learning approach for optimal segmentation and classification of skin cancer, with a focus on severity analysis. The proposed approach employs sophisticated image pre-processing techniques to remove noise while preserving essential features. These techniques ensure that the images used for analysis are of high quality, which is crucial for accurate diagnosis. The study focuses on extracting relevant features from these pre-processed images, leveraging innovative methods to capture complex patterns and dependencies within the data. For classification, the hybrid approaches integrates various advanced strategies to ensure robust and accurate identification of skin cancer types. These methods are designed to be efficient and effective, even in resource-constrained environments, addressing the computational challenges often associated with deep learning models. Furthermore, the study includes a comprehensive analysis of the severity of the identified cancers. The integration of these advanced techniques offers a holistic approach to skin cancer diagnosis, from initial detection to detailed severity analysis. The dataset is separated into training and testing. The experimental results performed on the HAM10000 dataset demonstrate that the proposed model can identify and predict skin diseases with 99.18% testing accuracy. Overall, the potential of advanced deep learning techniques to transform skin cancer diagnostics, offering a robust solution that enhances early detection, classification accuracy, and severity assessment, ultimately improving patient care and outcomes. To optimize model results, future investigations must take interpretability, dataset diversity, and the inclusion of medical metadata toward attention.
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