Method to Classify Skin Lesions using Dermoscopic images

21 Aug 2020  ·  Dusa Sai Charan, Hemanth Nadipineni, Subin Sahayam, Umarani Jayaraman ·

Skin cancer is the most common cancer in the existing world constituting one-third of the cancer cases. Benign skin cancers are not fatal, can be cured with proper medication. But it is not the same as the malignant skin cancers. In the case of malignant melanoma, in its peak stage, the maximum life expectancy is less than or equal to 5 years. But, it can be cured if detected in early stages. Though there are numerous clinical procedures, the accuracy of diagnosis falls between 49% to 81% and is time-consuming. So, dermoscopy has been brought into the picture. It helped in increasing the accuracy of diagnosis but could not demolish the error-prone behaviour. A quick and less error-prone solution is needed to diagnose this majorly growing skin cancer. This project deals with the usage of deep learning in skin lesion classification. In this project, an automated model for skin lesion classification using dermoscopic images has been developed with CNN(Convolution Neural Networks) as a training model. Convolution neural networks are known for capturing features of an image. So, they are preferred in analyzing medical images to find the characteristics that drive the model towards success. Techniques like data augmentation for tackling class imbalance, segmentation for focusing on the region of interest and 10-fold cross-validation to make the model robust have been brought into the picture. This project also includes usage of certain preprocessing techniques like brightening the images using piece-wise linear transformation function, grayscale conversion of the image, resize the image. This project throws a set of valuable insights on how the accuracy of the model hikes with the bringing of new input strategies, preprocessing techniques. The best accuracy this model could achieve is 0.886.

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Datasets


Results from the Paper


Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Lesion Classification HAM10000 Skin lesion classification using loss balancing and ensemble of multi-resolution efficient nets [gessert2020skin] Accuracy 92.6 # 3
Lesion Classification HAM10000 Skin lesion analysis towards melanoma detection using siamese network Accuracy 83.2 # 6
Lesion Classification HAM10000 ISIC 2019 Skin lesion analysis towards melanoma detection [a] Accuracy 85.1 # 5
Lesion Classification HAM10000 Classification Model - 2 with two path CNN model (The present project) Accuracy 88.6 # 4

Methods