Acne detection and severity by using convolutional neural network for internet of healthcare things (IoHT)

Nazri, Nur Farihah (2024) Acne detection and severity by using convolutional neural network for internet of healthcare things (IoHT). Project Report. Melaka, Malaysia, Universiti Teknikal Malaysia Melaka. (Submitted)

Text (Full text) Acne detection and severity by using convolutional neural network for internet of healthcare things (IOHT).pdf - Submitted Version Download (7MB)

Abstract

Acne Detection and Severity by Using Convolutional Neural Network for Internet of Healthcare Things is a mobile application that can identify the type of acne and their severity at the face. By using the integrated camera in smartphones, user can easily capture the image of their full fac and get the result of their acne condition immediately. Acne is a skin condition that happens when hair follicles under the skin become pore clogged. Pore clogging can produce blackheads, whiteheads and other types of pimples that usually happen to the face. There are several studies about acne detections using several deep learning methods to identify the severity of acne. By implying the artificial intelligence architecture, which is CNN, this project will be developing a classification model for acne based on the acne and lesion conditions to identify the severity of acne. This project aims to research severity of acne and how to treat it. To implement the classification model on mobile application that user-friendly to use for public with treatment suggestions. Finally, Acne Detection and Severity by Using CNN for Internet of Healthcare Things will be a mobile application that can seamlessly help people to identify the severity of acne on their face and suggesting an appropriate treatment that people can use to treat their acne correctly. Furthermore, this project can be a system that benefits people to get their result about their acne fast, effective treatment and less cost.

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