SEGMENTATION AND CLASSIFICATION OF LUNG CANCER USING CNN MODELS

Principal Investigator

Name
Ridwanullah Alabi

Degrees
Dr.

Institution
Kwara State University

Position Title
Student

Email
hi.ridwanullah@gmail.com

About this CDAS Project

Study
NLST (Learn more about this study)

Project ID
NLST-1277

Initial CDAS Request Approval
Jun 24, 2024

Title
SEGMENTATION AND CLASSIFICATION OF LUNG CANCER USING CNN MODELS

Summary
This research proposes a novel system for improved lung cancer analysis using Convolutional Neural Networks (CNNs). The system aims to achieve precise segmentation and classification of lung cancer regions within medical images.

Aims

Enhanced Segmentation Precision: Develop and implement a Mask R-CNN based segmentation model to accurately delineate lung cancer regions. This will ensure a more precise identification of cancerous areas compared to traditional methods.

Rigorous Evaluation: The model's segmentation accuracy will be meticulously evaluated by comparing the segmented areas with ground truth annotations provided by medical experts. This guarantees a reliable representation of cancerous regions.

Integrated Classification: CNN-based classification models will be incorporated to categorize the segmented lung cancer regions into specific classes. This will facilitate a detailed classification of various cancer types and stages, aiding in diagnosis and treatment planning.

Synergistic Optimization: The research will explore and optimize the collaboration between the segmentation and classification processes. This will ensure a more robust and efficient system that leverages the strengths of both techniques.

Collaborators

Ridwanullah Alabi - Kwara State University, Malete, Nigeria