TY  - JOUR
T1  - Lung Cancer Detection and Classification using Convolutional Neural Network
AU - Sajjanar, Deepa AU - Srinivasan, G.N. AU - Rekha, B.S. 
JO  - Asian Journal of Information Technology
VL  - 18
IS  - 10
SP  - 233
EP  - 240
PY  - 2019
DA  - 2001/08/19
SN  - 1682-3915
DO  - ajit.2019.233.240
UR  - https://makhillpublications.co/view-article.php?doi=ajit.2019.233.240
KW  - computer-aided diagnosis
KW  -Convolutional Neural Network (CNN)
KW  -Computed tomography
KW  -CT scan
KW  -pooling layer
AB  - In the field of medicine, identification and
treatment of cancer is considered as one of the biggest
challenge in treatment of chronic illness. The survival of
patients depends on timely detection and cure. Experts use
the CT scan or computed tomography scan images of
patients to detect and classify nodules, before proceeding
with advanced treatment procedures. The present day
advances in artificial intelligence, machine learning based
on deep learning model can be used to develop
sophisticated computer aided diagnosis systems to detect
cancerous nodules. The proposed system is based on
convolutional neural networks to categorize nodules
detected in CT scan images as malignant or benign. Image
processing and neural networks have been extensively
used in the detection and classification of cancerous
nodules. Hence, CNNs are more appropriate, for the task
of nodule detection and classification. CNN’s have more
properties like multiple feature extraction. When
convolution layer, subsampling or pooling layer, fully
connected layer such layers are combined, leading to
deep CNNs, it helps in increasing the accuracy of
classification. The proposed CNN Model will be suitable
for the early detection and classification of CT scans
images containing nodules with accuracy of 93.52% using
the domain knowledge of the CT scan images of lung in
the field of medicine and neural network.
ER  -