A Genetic Folding Strategy Based Support Vector Machine to Optimize Lung Cancer Classification

Abstract

Cancer is defined as an abnormal growth of human cells classified into benign and malignant. The site makes further classification of cancers of initiation and genomic underpinnings. Lung cancer displays extreme heterogeneity, making genomic classification vital for future targeted therapies. Especially considering lung cancers account for 1.76 million deaths worldwide annually. However, tumors do not always correlate to cancer as they can be benign, severely dysplastic (pre-cancerous), or malignant (cancerous). Lung cancer presents with ambiguous symptoms, thus is difficult to diagnose and is detected later compared to other cancers. Diagnosis relies heavily on radiology and invasive procedures. Different models developed employing Artificial Intelligence (AI), and Machine Learning (ML) have been used to classify various cancers. In this study, the authors propose a Genetic Folding Strategy (GFS) based model to predict lung cancer from a lung cancer dataset. We developed and implemented GF to improve Support Vector Machines (SVM) classification kernel functions and used it to classify lung cancer. We developed and implemented GF to improve SVM classification kernel functions and used it to classify lung cancer. Classification performance evaluations and comparisons between the authors' GFS model and three SVM kernels, linear, polynomial and radial basis function, were conducted thoroughly on real lung cancer datasets. While using GFS in classifying lung cancer, the authors obtained an accuracy of 96.2%. This is the highest current accuracy compared to other kernels.

Advanced mask region-based convolutional neural network based deep-learning model for lung cancer detection

“…First is the the target feature extraction, and second is Classifying and positioning the objects. Assigning a group of class labels to the items of interest in the input image is the main phase in the technique of image classification [19]- [20]. Mask R-CNN has numerous aspects that influence its performance [21]- [25], like the magnitude of the input image, the threshold for intersection over union (IoU) between predicted and ground truth boxes, and the definite quantity of regions of interest (ROIs) per image that are proposed by the model.…”

Enhancing Lung Cancer Classification Effectiveness Through Hyperparameter-Tuned Support Vector Machine

“…Study [41] also used a dataset [33] but applied the Genetic Folding Strategy (GFS) to enhance the kernel function in SVM classification to classify lung cancer. Performance evaluation and comparison were conducted with three types of SVM kernels on the actual lung cancer dataset, and the results showed an accuracy rate of 96.2%, which is the highest compared to the other kernels.…”

“…A low C value makes the hyperplane surface smoother, while a high C value tries to identify all training samples accurately, but a small C value will provide a wider margin with a higher error tolerance. Meanwhile, gamma determines how big the effect of a single training example is; the higher the gamma value, the closer the additional instances must be to be affected [41]. Another parameter is Probability, which produces probability estimates for each class, and the kernel used is the Radial Basis Function (RBF).…”