dermalens-skin-cancer-model

DermaLens Skin Cancer Classification Model

A Deep Learning-based Skin Lesion Classification Model trained on the HAM10000 dataset using TensorFlow and Keras.

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Overview

DermaLens is a Convolutional Neural Network (CNN) developed for automated skin lesion classification. The model analyzes dermoscopic skin images and predicts the most likely lesion category among seven classes from the HAM10000 dataset.

This project demonstrates the application of Deep Learning and Computer Vision techniques in healthcare and skin cancer screening research.

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Model Information

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Dataset

The model was trained using the HAM10000 (Human Against Machine with 10,000 Training Images) dataset.

Dataset Statistics

• 10,000+ dermoscopic images
• 7 skin lesion categories
• Medical imaging benchmark dataset
• Widely used in dermatology AI research

Supported Classes

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Model Architecture

The model is based on a Convolutional Neural Network (CNN) architecture consisting of:

• Convolution Layers
• ReLU Activation Functions
• Max Pooling Layers
• Batch Normalization
• Dropout Layers
• Dense Fully Connected Layers
• Softmax Output Layer

Image Preprocessing

Before inference, images undergo:

1. Image Resizing
2. Pixel Normalization
3. Batch Dimension Expansion

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Training Configuration

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Performance

Evaluation Results

Actual performance may vary depending on image quality, preprocessing pipeline, acquisition device, and dataset distribution.

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Usage

Installation

pip install tensorflow numpy pillow

Load Model

from tensorflow.keras.models import load_model

model = load_model("skin_cancer_cnn.h5")

Prediction Example

import numpy as np
from tensorflow.keras.preprocessing import image
from tensorflow.keras.models import load_model

model = load_model("skin_cancer_cnn.h5")

img = image.load_img("sample.jpg", target_size=(224, 224))
img = image.img_to_array(img)

img = np.expand_dims(img, axis=0)
img = img / 255.0

prediction = model.predict(img)

predicted_class = np.argmax(prediction)

print("Predicted Class:", predicted_class)

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Intended Use

Suitable For

• Educational Projects
• Research Experiments
• Computer Vision Learning
• Medical AI Demonstrations
• Academic Applications

Not Suitable For

• Clinical Diagnosis
• Patient Treatment Decisions
• Emergency Healthcare Applications
• Real-World Medical Deployment

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Limitations

• Trained exclusively on the HAM10000 dataset.
• Performance may degrade on images from unseen sources.
• Sensitive to image quality and preprocessing.
• Dataset imbalance may affect minority-class predictions.
• Not validated in clinical environments.

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Ethical Considerations

This model is intended solely for educational and research purposes.

Predictions generated by this model should not be interpreted as medical advice, diagnosis, or treatment recommendations.

Users should always consult qualified healthcare professionals for any medical concerns.

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Citation

If you use this model in your research or projects, please cite:

@misc{dermalens2026,
  title={DermaLens: Skin Cancer Classification using Deep Learning},
  author={Mitanshu Makwana},
  year={2026},
  publisher={Hugging Face}
}

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Contact

Author: Mitanshu Makwana

Project: DermaLens – Skin Cancer Detection System

Hugging Face: Mitanshu10/dermalens-skin-cancer-model

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Disclaimer

⚠️ This model is not FDA-approved, CE-certified, or clinically validated.

It is provided strictly for research, educational, and demonstration purposes and must not be used as a substitute for professional medical diagnosis.