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.

Ridwanullah Alabi - Kwara State University, Malete, Nigeria