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Mitanshu10/
dermalens-skin-cancer-model

Name: dermalens-skin-cancer-model
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kuchikihater/HAM10000

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DermaLens Skin Cancer Classification Model

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

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.

Model Information

Property	Value
Model Name	DermaLens Skin Cancer Classification Model
Author	Mitanshu Makwana
Framework	TensorFlow / Keras
Model Format	`.h5`
Model File	`skin_cancer_cnn.h5`
Model Size	533 MB
Dataset	HAM10000
Number of Classes	7
Task	Multi-Class Image Classification
Accuracy	~83%

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

Class Label	Disease Type
`akiec`	Actinic Keratoses and Intraepithelial Carcinoma
`bcc`	Basal Cell Carcinoma
`bkl`	Benign Keratosis-like Lesions
`df`	Dermatofibroma
`mel`	Melanoma
`nv`	Melanocytic Nevi
`vasc`	Vascular Lesions

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:

Image Resizing
Pixel Normalization
Batch Dimension Expansion

Training Configuration

Parameter	Value
Framework	TensorFlow / Keras
Optimizer	Adam
Loss Function	Categorical Crossentropy
Evaluation Metric	Accuracy
Classification Type	Multi-Class

Performance

Evaluation Results

Metric	Score
Accuracy	~83%
Classes	7

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

Usage

Installation

pip install tensorflow numpy pillow

Load Model

python

from tensorflow.keras.models import load_model

model = load_model("skin_cancer_cnn.h5")

Prediction Example

python

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)

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

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.

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.

Citation

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

bibtex

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

Contact

Author: Mitanshu Makwana

Project: DermaLens – Skin Cancer Detection System

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

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.