This the HAM10000 ("Human Against Machine with 10000 training images") dataset.It consists of 10015 dermatoscopicimages which are released as a training set for academic machine learning purposes and are publiclyavailable through the ISIC archive. This benchmark dataset can be used for machine learning and for comparisons with human experts. Here's the dataset link https://dataverse.harvard.edu/dataset.xhtml?persistentId=doi:10.7910/DVN/DBW86T
It has 7 different classes of skin cancer which are listed below :
Melanocytic nevi Melanoma Benign keratosis-like lesions Basal cell carcinoma Actinic keratoses Vascular lesions Dermatofibroma Here I will try to detect 7 different classes of skin cancer using Convolution Neural Network with keras tensorflow in backend and then analyse the result to see how the model can be useful in practical scenario. We will move step by step process to classify 7 classes of cancer.
In this kernel I have followed following 14 steps for model building and evaluation which are as follows :
Step 1: Importing Essential Libraries
Step 2: Loading data and Making labels
Step 3: Train Test Split
Step 4: Exploratory data analysis (EDA)
Step 5: Model Building (CNN)
Step 6: Setting Optimizer & Annealing
Step 7: Fitting the model
Step 8: Model Evaluation