Tehran University Medical Journal

Background: Skin cancer is one of the most common forms of cancer in the world and melanoma is the deadliest type of skin cancer. Both melanoma and melanocytic nevi begin in melanocytes (cells that produce melanin). However, melanocytic nevi are benign whereas melanoma is malignant. This work proposes a deep learning model for classification of these two lesions.   
Methods: In this analytic study, the database of HAM10000 (human against machine with 10000 training images) dermoscopy images, 1000 melanocytic nevi and 1000 melanoma images were employed, where in each category 900 images were selected randomly and were designated as the training set. The remaining 100 images in each category were considered as the test set. A deep learning convolutional neural network  (CNN) was deployed with AlexNet (Krizhevsky et al., 2012) as a pretrained model. The network was trained with 1800 dermoscope images and subsequently was validated with 200 test images. The proposed method removes the need for cumbersome tasks of lesion segmentation and feature extraction. Instead, the CNN can automatically learn and extract useful features from the raw images. Therefore, no image preprocessing is required. Study was conducted at Shahid Beheshti University of Medical Sciences, Tehran, Iran from January to February, 2020.
Results: The proposed model achieved an area under the receiver operating characteristic (ROC) curve of 0.98. Using a confidence score threshold of 0.5, a classification accuracy of 93%, sensitivity of 94%, and specificity of 92% was attained. The user can adjust the threshold to change the model performance according to preference. For example, if sensitivity is the main concern; i.e. false negative is to be avoided, then the threshold must be reduced to improve sensitivity at the cost of specificity. The ROC curve shows that to achieve sensitivity of 100%, specificity is decreased to 83%.
Conclusion: The results show the strength of convolutional neural networks in melanoma detection in dermoscopy images. The proposed method can be deployed to help dermatologists in identifying melanoma. It can also be implemented for self diagnosis of photographs taken from skin lesions. This may facilitate early detection of melanoma, and hence substantially reduce the mortality chance of this dangerous malignancy.

Background: The most common types of non-melanoma skin cancer are basal cell carcinoma (BCC), and squamous cell carcinoma (SCC). AKIEC -Actinic keratoses (Solar keratoses) and intraepithelial carcinoma (Bowen’s disease)- are common non-invasive precursors of SCC, which may progress to invasive SCC, if left untreated. Due to the importance of early detection in cancer treatment, this study aimed to propose a computer-based model for identification non-melanoma malignancies.
Methods: In this analytic study, 327 AKIEC, 513 BCC, and 840 benign keratosis images from human against machine with 10000 training dermoscopy images (HAM10000) were extracted. From each of these three types, 90% of the images were designated as the training set and the remaining images were considered as the test set. A deep learning convolutional neural network (CNN) was developed for skin cancer detection by using AlexNet (Krizhevsky, et al., 2012) as a pretrained network. First, the model was trained on the training images to discriminate between benign and malignant lesions. In comparison with conventional methods, the main advantage of the proposed approach is that it does not need cumbersome and time-consuming procedures of lesion segmentation and feature extraction. This is because CNNs have the capability of learning useful features from the raw images. Once the system was trained, it was validated with test data to assess the performance. Study was carried out at Shahid Beheshti University of Medical Sciences, Tehran, Iran, in January and February, 2020.