Electronic Health Data Analysis for Skin Cancer Prediction using BAT Algorithm

Author(s)	Mr. Abhishek Kumar Sahu, Prof. Manish Rohilla
Country	India
Abstract	Skin lesions refer to specific areas on the skin that display unusual growth patterns or appearances, which may be harmless (benign) or cancerous (malignant). Accurate identification and classification of these lesions are vital for the early detection and successful management of skin cancer. Nonetheless, current methods for distinguishing between malignant and non-malignant lesions using dermoscopic images often fall short in effectively extracting relevant features and are prone to overfitting, especially when dealing with imbalanced datasets. This study focuses on enhancing the classification accuracy of skin cancer by improving the quality and representation of features within dermoscopic images. This paper has proposed a model that filter noise from the input data and extract learning features. For feature extraction model has uses BAT genetic algorithm. BAT based selected pixels were used for the learning of convolutional model. Experiment was done real dataset and result shows that proposed Bat Algorithm Based Skin Cancer Detection (BASCD) has improved the classification accuracy of work.
Keywords	Digital Image Processing, Skin Cancer Diagnosis, Machine learning, Medical Image Diagnosis, Feature Extraction, Segmentation.
Field	Engineering
Published In	Volume 7, Issue 5, September-October 2025
Published On	2025-10-22
DOI	https://doi.org/10.36948/ijfmr.2025.v07i05.58574

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