https://github.com/jeongahyun/combnet

An image classification neural network using multi scale features.
https://github.com/jeongahyun/combnet

acc diagnosis multi-scale-features pytorch skin-diseases

Last synced: 8 months ago
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An image classification neural network using multi scale features.

• Host: GitHub
• URL: https://github.com/jeongahyun/combnet
• Owner: jeongahyun
• Created: 2020-06-22T05:48:50.000Z (over 5 years ago)
• Default Branch: master
• Last Pushed: 2021-01-28T03:53:05.000Z (over 4 years ago)
• Last Synced: 2023-10-20T20:43:25.598Z (almost 2 years ago)
• Topics: acc, diagnosis, multi-scale-features, pytorch, skin-diseases
• Language: Python
• Homepage:
• Size: 705 KB
• Stars: 5
• Watchers: 1
• Forks: 1
• Open Issues: 0
• Metadata Files:
  • Readme: readme.md

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README

          
# CombNet: Diagnosis of Skin Diseases using Multi-Scale Features

![result](imgs/result.png)  

  

This is a Pytorch implementation of CombNet. [(paper here)](https://www.dbpia.co.kr/pdf/pdfView.do?nodeId=NODE09410354&mark=0&useDate=&bookmarkCnt=0&ipRange=N&accessgl=Y&language=ko_KR)  

**CombNet** is a neural network created for image classification: conceived in the feature pyramid structure.

I used datasets from [kaggle - Skin Cancer MNIST: HAM10000](https://www.kaggle.com/kmader/skin-cancer-mnist-ham10000)  

  

***********************************

### Architecture

  

![architecture](imgs/architecture.png)

  

Backbone of CombNet is made up of ResNet-18, supported by two types of sub-network from different scale feature maps.  

The following formula is used to calculate the total loss from the three losses:  

  

![equation](https://latex.codecogs.com/gif.latex?loss_{main}:=loss_{main}+\alpha&space;loss_{1}+\beta&space;loss_{2})  

  

In this experiment, the best result was when alpha = 0.5, betha = 0.7  

From this model, you can get around 5% higher accuracy than the plain ResNet accuracy!  

  

![acc](imgs/accuracy_plot_a_05_b_07.png)

### Environment

The model is trained using following hardware:

* GTX TITAN X (Pascal) - 12GB VRAM

* Intel(R) Core(TM) i7-5930K CPU @ 3.50GHz

* 64GB RAM  

The code is developed under the following software:

* Ubuntu 16.04.6 LTS

* CUDA V10.1.243

* Python 3.6.10

* PyTorch 1.5.0