Abstract
Among all the types of cancer, skin cancer is one of the deadliest and its rise has been globally alarming. While this is curable with early detection, we have limited number of specialists and methodologies that can recognize this disease accurately. As trained specialists are limited, recognition of skin cancer and their classification into seven classes, viz., melanoma, dermatofibroma, basal cell carcinoma, actinic keratosis, benign keratosis, melanocytic nevi, vascular lesions, is relatively difficult. Towards this goal the authors have proposed an automated system which uses deep learning techniques using convolutional neural network (CNN) to detect and classify skin lesion images. The system is evaluated using 3 instances in which the model is trained for 7 classes in 3 instances. A sequential model helps in efficient detection and classification of the skin lesions into 7 different types. Among the 3 models trained and tested, an accuracy of 58% is achieved in under-sampled dataset. The average sampled dataset’s model and the over-sampled dataset’s model were the best performing model with highest accuracies of 83.42 and 94.37%, respectively.
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Acknowledgements
The authors would like to thank the School of Computer Science and Engineering and Centre for Cyber Physical Systems, Vellore Institute of Technology, Chennai, for giving the support and encouragement to proceed with the research and produce fruitful results.
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Pati, N.K., Asish, Y.S., Manoj Kumar, K. et al. Oversampled Two-dimensional Deep Learning Model for Septenary Classification of Skin Lesion Disease. Natl. Acad. Sci. Lett. 46, 159–164 (2023). https://doi.org/10.1007/s40009-022-01175-x
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DOI: https://doi.org/10.1007/s40009-022-01175-x