Skin Cancer Detection | 1. Dataset Prep(augmentation, Noise Removal, etc)

[Sindhu-2004]

Is your feature request related to a problem? Please describe.

A skin cancer detection model using TensorFlow to classify images as malignant or benign.

Describe the solution you'd like

Dataset :
[Skin Cancer Dataset] https://www.kaggle.com/datasets/nodoubttome/skin-cancer9-classesisic

Approach : In this skin cancer detection project we implement a convolutional neural network (CNN) with the EfficientNetB7 architecture, leveraging transfer learning for improved performance. By loading a dataset of skin images labeled as malignant or benign, using Pandas and Numpy for data handling and Matplotlib for visualization. The dataset is split into training and validation sets, with images preprocessed by resizing and normalizing pixel values. EfficientNetB7, pre-trained on ImageNet, serves as the backbone for feature extraction, with its layers frozen to retain learned weights. The model is built using Keras Functional API, incorporating layers for flattening, dense connections with 256 units each, ReLU activation functions for non-linearity, Batch Normalization layers for training stabilization, and a Dropout layer with a 0.3 rate to prevent overfitting. The final layer is a single neuron with a sigmoid activation function, producing a probability score for malignancy. The model is compiled using the Adam optimizer and binary cross-entropy loss, with AUC as the evaluation metric. Training is conducted over multiple epochs, followed by performance evaluation through plots of training and validation loss and AUC metrics, demonstrating the model's effectiveness in distinguishing between malignant and benign skin lesions. This approach showcases the utility of transfer learning and advanced CNN architectures in medical image classification tasks.

Describe alternatives you've considered

No response

Additional context

No response

Code of Conduct

• I agree to follow this project's Code of Conduct

[github-actions]

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[saikrishna823]

I would like to contribute to this issue.I have experience of working with Machine Learning and deep learning.I will use cnn,and other pretrained models for prediction.I am GSSOC 2024 participant.

[SrijanShovit]

@Sindhu-2004 proceed with dataset prep first;

[SrijanShovit]

I find your approach just copied from some AI stuff. If yes, don't repeat it again.

[saikrishna823]

@SrijanShovit ,can you assign any issues to me?

[a-di-ti]

Hi, looking forward to being assigned this issue for further contribution!

[SrijanShovit]

@saikrishna823 Your approach?

[saikrishna823]

First I will construct general CNN architecture then I will use pretrained models like VGG,Resnet and will also explore other pertained models for better accuracy.

[SrijanShovit]

How can you make sure all your models are less than 5 MB? Any plans for that?

[Taranpreet10451]

Can you please assign this to me.

[github-actions]

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