AI-Driven Skin Cancer Detection Application
using ML for early skin cancer detection.
Accuracy and Metrics
The trained skin cancer detection model achieved an accuracy form 53% to over 90% on the validation set. It demonstrated high precision and recall for both benign and malignant cases, indicating its effectiveness in diagnosis.
The web-based application received positive feedback from 37 dermatologists and users alike. Its user-friendly interface and accurate predictions contributed to its adoption and usage for preliminary assessments of skin lesions.
By providing an accessible tool for early skin cancer detection, the application has the potential to aid in early intervention and improve the chances of successful treatment.
The model continues to be updated with new data to enhance its accuracy and keep up with evolving medical knowledge. User feedback and additional features, such as a skin lesion severity score, are being considered for future iterations of the application.
Creation of a user-friendly web-based interface using Django. Integration of the trained model into the application. Ability for users to upload mole images for classification. Displaying the classification results with an indication of the likelihood of malignancy.
Feature Extraction with ResNet50
Utilization of the ResNet50 model pre-trained on a large dataset to extract high-level features from moles images. Removal of the top classification layers to retain the feature extraction capability.
Development of a custom neural network classifier using Keras. The classifier takes ResNet50 features as input and outputs skin cancer classifications (benign or malignant). Training the classifier on the preprocessed dataset.
Evaluation of the model's performance using metrics such as accuracy, precision, recall, and F1-score on the validation set. Hyperparameter tuning to optimize the model's architecture and training parameters.
Data Collection and Preprocessing
A curated dataset of moles images obtained from various sources. Splitting the dataset into training, validation, and test sets. Preprocess the images through resizing (500x500), format normalization (converting to RGB), and data augmentation to improve model generalization.
A dataset of moles images labeled as benign or malignant
Machine Learning Frameworks
Scikit-learn and Keras
ResNet50 as a feature extractor
Django for creating a web-based user interface