An effective approach to identification and classification of lung cancer from CT images based on deep learning models

Abstract

Lung cancer is the leading cause of cancer death in the globe. Early identification of lung cancer can help to prevent lung cancer from becoming chronic, severe and life threatening. Here, CT images are frequently used and an automated and appropriate method using deep learning technique that can potentially makes a huge contribute to make quick and accurate diagnosis for lung cancer. However, in the area of medical imaging using deep learnings techniques, there have two limitations. One huge training time and the other one is insufficient and imbalanced datasets. This study will present the number of image balancing and reducing the overall processing time. The dataset we use in this research work contains three cases normal, benign, and malignant. In the dataset, we use data augmentation techniques to increase the amount of data then we apply some image processing method on the dataset including some filter like GaussianBlur for reduce noise, Adaptive Thresholding for high component details and edge, lastly Image Negative and Bit Plane Slicing. We proposed a Customized Convolutional Neural Network (CCNN) model using 224 x 224 size images classify the lung cancer into three classes. Seven transfer learning models, VGG16, VGG19, ResNet50, ResNet101, DenseNet201, EfficientNetB4 and MobileNetV2 are applied with the same image and batch size and all the transfer leaning models are compared with the proposed CCNN model and we got the maximum test accuracy of 98.18% from CCNN model including the require time for per epoch 2 sec. Our proposed model may help medical expert for diagnosis the lung cancer from medical CT images and our aim to add more data in the dataset and use more deep learning models in our future study.