Lung Cancer Detection using CNN

Lung Cancer Detection using CNN

Mr.  Abhay Chavan1, Mr.  Ajay Jadhav2, Mr. Aditya Gite3, Mr.  Shrinivas Bijja4

Guide of: Prof. Kirti G. Walke5

Undergrad. Student, Dept. of Information Technologies SKN Sinhgad Institute of Technology & Science,

Lonavala, Maharashtra

Abhay Chavan, abhaychavan.sknsits.it@gmail.com

Aditya Gite,  adityagite.sknsits.it@gmail.com

Ajay Jadhav, ajayjadhav.sknsits.it@gmail.com

Shrinivas Bijja, shrinivasbijja.sknsits.it@gmail.com

Abstract:  In recent years, automatic detection of defects in CT scan images has become crucial due to increasing lung-related issues caused by declining air quality. To effectively treat conditions like lung cancer, thorough examination of CT images, particularly pulmonary nodules, is essential. Machine learning plays a key role in this process, efficiently distributing the workload and processing large datasets to yield accurate results. The diagnostic approach involves three phases: CT image pre-processing, deep learning, and CNN utilization. Pre-processing converts raw data, deep learning assigns data significance, and CNNs determine lung health status, distinguishing between normal and abnormal conditions.

Keywords:  Feature Extraction CNN, Classification, Model-training