2692-4048ISSN (Online)
2770-0070ISSN (Print)
Volume 17 , Issue 1 , PP: 124-134, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Nyemeesha .V 1 * , M. Kavitha 2
Doi: https://doi.org/10.54216/FPA.170109
The incidence of cancer cases has been rising rapidly over the last few decades. Skin cancer is one of the widely found types of cancer, is further classified into two main types, Melanoma and Non-Melanoma. Though Melanoma is less common than other types of skin cancer, it can be lethal if not treated promptly. But it is not the only type of skin lesion that needs attention. It becomes necessary to promptly identify and classify the skin lesions for the recovery of the patient. The machine learning models of Deep Learning prove to be very efficient in this regard. Hence, we developed a deep learning model which is an ensemble of InceptionV3, Xception and ResNet152 models. It can classify the skin lesions into seven main types -Melanoma, Melanocytic Nevi, Benign Keratosis-like lesions, Basal cell carcinoma, actinic keratosis, vascular lesions, Dermatofibroma. The method was applied to dermoscopic images from the HAM10000 dataset. The presence of noise and artifacts in the images makes it difficult to classify. So, as a preprocessing step, we performed hair removal on the dermoscopic images which is a series of methods that starts with blackhat filtering, subsequently creating a mask for inpainting and then applying the inpainting algorithm. Further Contrast enhancement was performed by applying the Contrast Limited Adaptive Histogram Equalization (CLAHE) algorithm on the luminance channel of HSV image to improve the contrast of the image and also makes sure that it is not over-amplified. It is then followed by Skin Lesion Segmentation where a grabcut algorithm is applied on the enhanced image which segments the image. Thus, the segmented images are produced which are fed to the Model for training and testing. To cope up with the unbalanced dermoscopy image dataset available, we performed Image augmentation on the images generated in the previous step which alters the existing images to create some more images for the model training process, thus solving the problem of paucity of dataset and substantially increases the performance of the model. The final dataset generated is fed to the three deep learning models InceptionV3, Xception and Resnet152 which achieved an accuracy of 84.6%, 86.5% and 86.7% respectively. These were later given to two different ensemble models - Stacking and Random Forest. The Stacking model achieved an accuracy of 88.6% and Random Forest achieved an accuracy of 92.59%. The proposed system includes a GUI for a good user experience.
Cancer , Prediction , Deep learning , Dermoscopic images , Augmentation
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