Melanoma image classification based on MobileNetV2 network
Rarasmaya Indraswari, Rika Rokhana, Wiwiet Herulambang
Abstract
Melanoma is one of the most common types of cancer that can lead to high mortality rates if not detected early. Recent studies about deep learning methods show promising results in the development of computer-aided diagnosis for accurate disease detection. Therefore, in this research, we propose a method for classifying melanoma images into benign and malignant classes by using deep learning model and transfer learning. MobileNetV2 network is used as the base model because it has lightweight network architecture. Therefore, the proposed system is promising to be implemented further on mobile devices. Moreover, experimental results on several melanoma datasets show that the proposed method can give high accuracy, up to 85%, compared with other networks. Furthermore, the proposed architecture of the head model, which uses a global average pooling layer followed by two fully-connected layers, gives high accuracy while maintaining the network’s efficiency.