Skin Cancer Detection using Hybrid Neural Network Model

Mrityunjoy Biswas, Shobuj Chandra Das, Shajib Kumar Shaha, Kazi Hasan Al Banna and Eftear Rahman
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Mrityunjoy Biswas: Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, 1216, Bangladesh
Shobuj Chandra Das: Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, 1216, Bangladesh
Shajib Kumar Shaha: Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, 1216, Bangladesh
Kazi Hasan Al Banna: Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, 1216, Bangladesh
Eftear Rahman: Department of Computer Science and Engineering, Bangladesh University of Business and Technology, Dhaka, 1216, Bangladesh

International Journal of Research and Innovation in Social Science, 2023, vol. 7, issue 12, 26-36

Abstract: Skin cancer, the fifth most frequent malignancy worldwide, burdens global health and the economy. Skin cancer rates have increased due to rapid environmental change, indus-trialization, and genetic alteration. This work introduces deep learning on dermatological pictures to identify skin cancer. We gathered 18274 skin photos, both cancerous and not. Scaling, augmenting, and normalizing data provided model robustness. Our skin cancer detection model employs CNNs and LSTMs. We retrieved skin characteristics using the InceptionResNetV2 pre-trained model to enhance discriminative feature learning. Batch normalization and dropout layers prevented overfitting. Our model performed well in training epochs using optimization callbacks. The accuracy was 93.41%, AUC 85.09%, precision 94.03%, and recall 99.10%. However, the model included 211 false positives and 30 negatives. These results show the model can identify skin cancer. These studies show that deep learning systems can diagnose skin cancer. The model’s accuracy and low false positive rate help dermatologists and healthcare providers. This technology may improve skin cancer detection and treat-ment by reducing manual diagnostic subjectivity and accelerating assessments.

Date: 2023
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