Automated Malignant Melanoma Classification Using Convolutional Neural Networks

Abstract

This research is proposed a design of architecture for melanoma (a kind of skin cancer) recognition by using a Convolutional Neural Network (CNN), work that will be useful for researchers in future projects in areas like biomedicine, machine learning, and others related moving forward with their studies and improving this proposal. CNN is mostly used in computer vision (a branch of artificial intelligence), applied to pattern recognition in skin moles and to determine the existence of malignant melanoma, or not, with a limited dataset. The CNN classifier designed and trained in this case was built through a couple of layers of convolution and pooling stacked to form a neural network of 6 layers followed by the fully connected to complete the architecture with an output classifier. The proposed database to train our CNN is the largest publicly collection of dermoscopic images of melanomas and other skin lesions, provided by the International Skin Imaging Collaboration (ISIC), sponsored by International Society for Digital Imaging of the Skin (ISDIS), an international effort to improve melanoma diagnosis. The purpose of this research was to design a Convolutional Neural Network with a high level of accuracy to help professionals in medicine with a melanoma diagnosis, in this case, it was possible to get accuracy up to 88.75 %.

Embedded artificial intelligence system using deep learning and raspberrypi for the detection and classification of melanoma

“…3. Comparison with the existing work Reference Model Accuracy (%) [39] CNN with 9 layers 80.52 [40] Modified GoogleNet 81 [41] CNN+SVM 89.52 [42] CNN 87.82 [43] MobilenetV2-LSTM 90.72 [44] DenseNet-EfficientNet 85.80 Proposed Deep CNN 92…”

“…The authors obtained an accuracy of 89.52%. Guarnizo et al [42] proposed a model for diagnosis skin cancer using the deep learning algorithm CNN. This CNN model contains a lot of hidden layers.…”