数据集获取

git clone https://github.com/ARUNJOGLE/Melanoma_Detection.git

curl -L -o repo.zip https://github.com/ARUNJOGLE/Melanoma_Detection/archive/refs/heads/main.zip
unzip repo.zip

源站 README 摘录（使用方式）

Melanoma Skin Cancer Detection

Abstract

In cancer, there are over 200 different forms. Out of 200, melanoma is the deadliest form of skin cancer. The diagnostic procedure for melanoma starts with clinical screening, followed by dermoscopic analysis and histopathological examination. Melanoma skin cancer is highly curable if it gets identified at the early stages. The first step of Melanoma skin cancer diagnosis is to conduct a visual examination of the skin’'s affected area. Dermatologists take the dermatoscopic images of the skin lesions by the high-speed camera, which have an accuracy of 65-80% in the melanoma diagnosis without any additional technical support. With further visual examination by cancer treatment specialists and dermatoscopic images, the overall prediction rate of melanoma diagnosis raised to 75-84% accuracy. The project aims to build an automated classification system based on image processing techniques to classify skin cancer using skin lesions images.

Problem Statement

In the skin biopsy, the dermatologist takes some part of the skin lesion and examines it under the microscope. The current process takes almost a week or more, starting from getting a dermatologist appointment to getting a biopsy report.
The aims to shorten the current gap to just a couple of days by providing the predictive model.
The approach uses Convolutional Neural Network (CNN) to classify nine types of skin cancer from outlier lesions images. This reduction of a gap has the opportunity to impact millions of people positively.

Motivation

The overarching goal is to support the efforts to reduce the death caused by skin cancer. The primary motivation that drives the project is to use the advanced image classification technology for the well-being of the people. Computer vision has made good progress in machine learning and deep learning that are scalable across domains.

Dataset

The dataset consists of 2357 images of malignant and benign oncological diseases, which were formed from the International Skin Imaging Collaboration (ISIC). All images were sorted according to the classification taken with ISIC, and all subsets were divided into the same number of images.
The data set contains the following diseases:
To overcome the issue of class imbalance, used a python package Augmentor (https://augmentor.readthedocs.io/en/master/) to add more samples acros

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

完整仓库：github.com/ARUNJOGLE/Melanoma_Detection