Triple SVM Integrated with Enhanced Random Region Segmentation for Classification of Lung Tumors

Abstract

The rapid growth of Computer vision and Machine Learning applications, especially in Health care systems, assures a secure, innovative lifestyle for society. The implication of these technologies in the early diagnosis of lung tumors helps in lung cancer detection and promises the survival rate of patients. The existing general diagnosis method of lung radiotherapy, i.e., Computed Tomography imaging (CT), doesn't spot exactly affected parts during injuries on lung malignancy. Herein, we propose a computer vision-based diagnostic method empowered with machine learning algorithms to detect lung tumors. The primary objective of the proposed method is to develop an efficient segmentation method to enhance the classification accuracy of lung tumors by implementing a Triple Support Vector Machine (SVM) for the classification of data samples into normal, malignant, or benign, Random Region Segmentation (RSS) for image segmentation and SIFT and GLCM algorithms are applied for feature extraction technique. The model is trained considering the dataset IQ -OTH or NCCD with 300 epochs, with an accuracy of 96.5% achieved under 200 cluster formations.

An optimized convolutional neural network architecture for lung cancer detection

“…Since EESNN lacked optimization for ideal parameters, they developed a flamingo search optimization algorithm to optimize EESNN for accurate lung cancer classification. Gowda and Jayachandran 14 proposed lung tumor detection using a computer vision-based diagnostic approach enhanced by machine learning techniques. The main goal of the proposed method is to create an effective segmentation technique that will improve the accuracy of the classification of lung tumors.…”