An Improved Convolution Neural Network and Modified Regularized K-Means-Based Automatic Lung Nodule Detection and Classification

Abstract

If lung cancer is not detected in its initial phases, it can be fatal. However, because of the quantity and structure of its nodules, lung cancer is difficult to detect early. For accurate detections, radiologists require assistance from automated tools. Numerous expert methods have been created over time to assist radiologists in the diagnosis of lung cancer. However, this requires accurate research. Therefore, in this article, we propose a framework to precisely detect lung cancer by categorizing it between benign and malignant nodules. To achieve this objective, an efficient deep-learning algorithm is presented. The presented technique consists of four stages, namely pre-processing, segmentation, classification, and severity stage analysis. Initially, the collected image is given to the pre-processing stage to eliminate the distortion present in the image. Then, the noise-free image is given to the segmentation stage. For segmentation, in this paper, modified regularized K-means (MRKM) clustering algorithm is presented. After the segmentation process, the segmented nodule image is fed to the classification stage to categorize the nodule as benign or malignant (risk nodule). For classification, an improved convolution neural network (ICNN) is presented. The proposed ICNN is designed by modifying CNN with the integration of the adaptive tree seed optimization (ATSO) algorithm. Finally, the stage identification is carried out based on the size of the nodule and we classify the malignant nodule as S1-S4. The presented technique attained the maximum accuracy of 96.5% and performance compared with existing state-of-art methods.

Keywords: Adaptive tree seed optimization; Improved convolution neural network; Lung nodule; Modified regularized K-means; Severity.

Fig. 1

System architecture of proposed methodology

Fig. 2

Basic structure of CNN

Fig. 3

Pooling process

Fig. 4

Optimized CNN classifier-based lung nodule prediction

Fig. 5

Experimental used sample images

Fig. 6

Experimental results of segmentation stage. a Input image, b ground truth image, c output image, and d final output

Fig. 7

Segmentation output for three varied positions. a Input image, b ground truth, c output image, and d final output image

Fig. 8

Confusion matrix. a ICNN, b KNN, c ANN, d SVM, and e CNN

Fig. 9

Loss vs epochs

Fig. 10

Accuracy vs epochs

Fig. 11

Epochs vs fitness