Page 127 - Abstract book - TJSSF-2020

Thailand – Japan Student Science Fair 2020 (TJ-SSF 2020)
“Seeding Innovations through Fostering Thailand – Japan Youth Friendship”

SkinFine Web Application for Skin Disease Classification Using Machine
Learning

Waranthorn Chansawang , Tanakrit Iamvilai
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1
Advisors: Khunthong Klaythong , Wasit Limprasert
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1

Princess Chulabhorn Science High School Pathum Thani
1
Thammasat University
2

Abstract

Skin diseases consist of both dangerous and non-dangerous types. Some of the non-dangerous types do
not need consultation because they can restore itself until they heal, for example, Atopic Dermatitis
found in Thailand 10-20% in children and 1-3% in adults, while some skin diseases can be lethal. But
normal people are uninformed about the danger of their current skin diseases so they panic to go to the
hospital to know how to treat, prompting congestion, and this affects the amount of work of medical
personnel i.e. doctors, nurses, and receptionists to increase. Therefore, having software that is easily
accessible and helps to classify skin diseases can be beneficial in many cases such as reducing hospital
congestion, reducing pollution problems from using vehicles to the hospital, and not wasting time to
see a doctor. Based on the preliminary information, we are interested in developing a web application
for skin disease classification using machine learning. The first step is a skin disease study, collecting
the dataset and then data augmentation to add diversity to the dataset. The second step is to develop a
model for the classification of dermatitis in the HAM10000 dataset without Melanocytic nevi class
using Convolutional Neural Network, the DenseNet121 model, which we divided into three
experiments, using oversampling, adjusting class weights, using the focal loss to solve the imbalanced
dataset problem. resulting in F1-score of 0.80, 0.84 and 0.83 respectively. Next step we transfer the
weights from these models to our dataset that is top 10 most common skin diseases in Thailand but
collected from 3 types including Atopic Dermatitis, Psoriasis, and Seborrheic Keratosis which we get
F1-score of 0.92, 0.86 and 0.92 respectively. After that, we ensemble the models using an arithmetic
mean of each model prediction and get an F1-score of 0.97. And lastly we bring all 3 models to develop
into a web application using Flask, HTML, CSS, and JavaScript. The results of the study showed that
the web application developed It can help patients analyze whether they should see a doctor or not.
Along with telling the patient how to take care of themself first by taking a picture of skin disease and
uploading them on the web application both on smart phones and computers.
Keywords: web application, skin disease, machine learning, transfer learning, ensemble

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