Multi-Class Skin Disease Classification using Deep Learning
Classifies dermatological images into multiple skin-condition categories using a fine-tuned EfficientNet model, achieving 92% top-1 accuracy on a benchmark skin-lesion dataset.
- Project code
- AUX-MLR-1909
- Domain
- Machine Learning
- Type
- Software project
- Level · Year
- Advanced · 2025-26
Technologies and components
What is included
- Complete source code
- Database / dataset and setup guide
- Project report (college format)
- PPT for review and viva
- Installation and demo support
- Viva question guidance
More Machine Learning Projects
Handwritten Digit and Signature Recognition using Deep Learning
Trains a CNN on the MNIST dataset and a custom signature corpus to recognise handwritten digits and verify signature authenticity for doc…
View project →Machine Learning · AdvancedReal-Time Object Detection for Autonomous Vehicles using YOLO
Detects pedestrians, vehicles and traffic signs in real time from dashcam footage using a YOLOv8 model optimised for edge-device inferenc…
View project →Machine Learning · IntermediateSpeech Emotion Recognition using Machine Learning
Classifies speaker emotion from voice-recording features like pitch and MFCCs using an SVM and CNN ensemble, achieving 87% accuracy acros…
View project →Machine Learning · BeginnerPlant Species Classification using Convolutional Neural Networks
Classifies leaf images into plant species using a fine-tuned CNN, packaged as a mobile-friendly app to help farmers and botanists identif…
View project →Machine Learning · AdvancedPredictive Maintenance for Industrial Machinery using ML
Predicts equipment failure from vibration and temperature sensor time-series using a gradient-boosted survival model, giving maintenance …
View project →Machine Learning · BeginnerCustomer Review Rating Prediction using Machine Learning
Predicts a numeric star rating from review text alone using TF-IDF features and a gradient-boosting classifier, validated on an e-commerc…
View project →Frequently asked questions
Can I get the Multi-Class Skin Disease Classification using Deep Learning project for my final year?
Yes. This project (code AUX-MLR-1909) is available as a software project kit for the 2025-26 academic year. Contact us on WhatsApp to request it.
Can this project be customised?
Yes. Features, technology and documentation can be modified to suit your guide and college requirements.
Which departments is this project suitable for?
CSE, IT, AI & DS, BCA and MCA students, and ECE students interested in software.
Get this project with full support
Ask about customisation, delivery time and viva support.
