HARNESSING DEEP LEARNING FOR LUNG CANCER DETECTION USING CT SCAN IMAGES

Anuradha Rani Choudhury, Satya Ranjan Panda, Jyotirmayee Rautaray, Pranati Mishra, Meenakshi Kandpal

DOI Number
https://doi.org/10.2298/FUEE2504639C
First page
639
Last page
661

Abstract


Lung cancer persists to be considered as the primary cause of rising mortality rates worldwide with clinical projection depending upon early-stage detection. Detection at the preliminary stage is crucial for treatment of the disease also to further the prognosis process. In this study IQOTHNCCD dataset is used. An innovative model is constructed for distinguishing images into three categories. The dataset comprises of three types of images such as malignant images, benign images and normal images. Preprocessing of the images is done with intensive steps in order to remove unwanted data. Deep learning along with transfer learning have been applied to the image dataset for classifying the images into various categories. In biomedical image classification deep learning serves as the most effective technique for classification and detecting the abnormal pulmonary nodules. Techniques such as InceptionV3, ResNet50, VGG16, VGG19, MobileNetV2 are evaluated through experiment for ensuring the credibility of the designed model. Key performance indicators were used which includes accuracy, sensitivity, precision and F1 score. The results obtained through the proposed model which is the custom CNN model yielded an accuracy of 98.79%, sensitivity of 98.97%, precision of 96.6% and F1 score of 97.7%.


Keywords

Deep Learning, Transfer Learning, Custom CNN, InceptionV3, ResNet50, VGG16, VGG19, MobilenetV2

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References


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