9/1/23, 12:17 AM
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AMITY UNIVERSITY
-----UTTAR PRADESH----Amity School of Engineering and Technology
Minor Project
Student Name
Enrollment No
Programme
Company's Name and
Address
Industry Guide
Name
Designation
Contact Number
PAARTH BHARDWAJ
A2305220552
B.Tech (Computer Science & Engineering)
Amity University Uttar Pradesh
Amity Rd, Sector 125, Noida, Uttar Pradesh 201301
110058
Dr. Geetika
Associate Professor
Ph.(O) : 9871882460
Mobile : 9871882460
Fax : 9871882460
E-mail : ait@amity.edu
(R) : 9871882460
Project Information
1) Project Duration : (68 Days)
a) Date of Summer Internship commencement (31/07/2023)
a) Date of Summer Internship Completion (06/10/2023)
2) Topic
SkinSage XAI : Intelligent Lesion Diagnosis
3) Project Objective
The "SkinSage XAI" skin lesion detection system utilizes deep learning models and Explainable AI
techniques to accurately classify skin lesions. Over ten weeks, the project team trains models, extracts
features, and implements Explainable AI methods. The system aims to enhance early skin lesion detection
for improved medical diagnosis and treatment.
4) Methodology to be adopted
• Data Collection and Cleaning: Gather a diverse dataset of skin lesion images from reliable sources and
perform data cleaning to ensure consistency and quality. • Deep Learning Model Training: Train multiple
deep learning models, such as CNN, ResNet, DenseNet, and InceptionV3, using transfer learning and finetuning on the collected dataset. • Feature Extraction: Extract deep features from the trained models and
apply Principal Component Analysis (PCA) to reduce dimensionality (optional). • Explainable AI
Implementation: Utilize Grad-CAM to visualize relevant regions in images and LIME to generate local
explanations for model predictions. • Interpretation and Documentation: Analyze the results, interpret model
behavior, and document findings to provide valuable insights for dermatologists.
5) Brief Summery of project(to be duly certified by the industry guide)
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The "SkinSage XAI" system is expected to achieve high accuracy in classifying skin lesions and provide
interpretable explanations for its predictions. The use of Explainable AI methods will offer visualizations of
relevant regions within images, empowering dermatologists to make informed decisions based on the
model's output. Through the development of the "SkinSage XAI" skin lesion detection system, the project
aims to make a significant contribution to the field of dermatology. By combining state-of-the-art deep
learning models with Explainable AI techniques, the system seeks to improve the early detection of skin
lesions, ultimately leading to more effective medical interventions and better patient outcomes.
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