MediSpot–Lung Tumor Detection using CNN

MediSpot–Lung Tumor Detection using CNN

Vikram Kailash Joshi

BE in Information Technology

Vidyalankar Institute of Technology

vikram.joshi20@vit.edu.in

Dr. Vidya Chitre, Professor

Department of   Information Technology

Vidyalankar Institute of Technology

vidya.chitre@vit.edu.in

Anujkumar Baghel

BE in Information Technology

Vidyalankar Institute of Technology

anujkumar.baghel@vit.edu.in

Tanush Shetty

BE in Information Technology

Vidyalankar Institute of Technology

tanush.shetty@vit.edu.in

Abstract—This abstract presents a novel approach to lung cancer detection using Convolutional Neural Networks (CNNs), renowned for image analysis. The system processes diverse medical images, undergoes meticulous preprocessing, and employs a custom-designed CNN architecture with convolutional and fully connected layers. Trained on labeled datasets, the CNN autonomously learns to distinguish cancerous from non-cancerous regions. Rigorous testing confirms high proficiency, offering advantages of automation, speed, and reduced human error. The system, once accurate, can be deployed in clinical settings to expedite precise diagnoses, promising improved patient outcomes through early intervention.Overall, our approach holds promise for significantly enhancing the efficiency and accuracy of lung cancer diagnosis, ultimately resulting in improved patient outcomes through early intervention and tailored treatment strategies

Keywords—classification; convolutional neural network; deep learning; lung cancer; medical images; segmentation.