Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

• Published in: Sensors (Basel, Switzerland) Vol. 21; no. 8; p. 2852
• Main Authors: Srinivasu, Parvathaneni Naga, SivaSai, Jalluri Gnana, Ijaz, Muhammad Fazal, Bhoi, Akash Kumar, Kim, Wonjoon, Kang, James Jin
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 18.04.2021; MDPI
• Subjects: Accuracy; Artificial intelligence; Back propagation; Classification; Datasets; Deep Learning; Disease; Genetic algorithms; grey-level correlation; Humans; Long Short-Term Memory (LSTM); MobileNet V2; Morphology; neural network; Neural networks; Neural Networks, Computer; Skin; skin disease; Skin Diseases; Support vector machines; deep learning; MobileNet; grey-level correlation; MobileNet V2; skin disease; Long Short-Term Memory (LSTM); mobile platform; neural network; Convolutional Neural Network (CNN)

Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be...