Skin-Cancer-Detection using python and CNN

By Datla Krishna Karthik varma

creating a skin cancer detection model in Google Colab using the Ham10000 dataset for precise and effective skin cancer detection.

The project's goal is to create a skin cancer diagnosis algorithm using Google Colab's Ham10000 dataset. The steps in the procedure are as follows:

1st link your kaggle account with google colab and download the dataset in to it using kaggle json.

Data preparation involves importing and preprocessing the Ham10000 dataset, which includes pictures of various skin lesions. Images must be resized, pixel values must be normalised, and data must be divided into training and testing sets.

Model Selection: For the goal of detecting skin cancer, an appropriate deep learning model, such as Convolutional Neural Network (CNN), is selected. The capacity of CNNs to extract hierarchical characteristics from images is well recognised.

Model Training: Using the training dataset, the chosen model is trained. The model gains the ability to generate predictions and extract useful features from photos of skin lesions during training.

Model Evaluation: The testing dataset is used to assess the trained model. This process aids in evaluating the model's performance in terms of recall, accuracy, and other crucial metrics.

Optimisation and fine-tuning: If the performance of the model is not adequate, the hyperparameters are adjusted, and methods like data augmentation or transfer learning are used to enhance the outcomes.

Testing and deployment: The last model is used to forecast skin cancer on brand-new, unused photos. Its effectiveness and accuracy are validated using data from the real world.

Iterative Improvement: The model and method can be improved and iterated upon to obtain greater performance and usability based on the testing results and user feedback.

The main objective of this research is to develop a reliable and accurate system for detecting skin cancer, which can help with early diagnosis and possibly save lives.