Human lung cancer classification and comprehensive analysis using different machine learning techniques

• Published in: Microscopy research and technique 2025-01, Vol.88 (1), p.234-250
• Main Authors: Priyadarshini, K., Ali, S. Ahamed, Sivanandam, K., Alagarsamy, Manjunathan
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Automation; Bayes Theorem; Classification; Computed tomography; Datasets; Decision Trees; Humans; Image processing; Image Processing, Computer-Assisted - methods; k‐nearest neighbors; Learning algorithms; Lung - diagnostic imaging; Lung - pathology; Lung cancer; Lung diseases; Lung Neoplasms - classification; Lung Neoplasms - diagnostic imaging; Lung Neoplasms - pathology; Machine Learning; Magnetic resonance imaging; Medical imaging; multi‐layer perceptron; Performance evaluation; random forest; Sensitivity analysis; stochastic gradient descent; Stochasticity; Support Vector Machine; Support vector machines; Tomography, X-Ray Computed - methods
• ISSN: 1059-910X; 1097-0029; 1097-0029
• DOI: 10.1002/jemt.24682

Lung cancer is the most common causes of death among all cancer‐related diseases. A lung scan examination of the patient is the primary diagnostic technique. This scan analysis pertains to an MRI, CT, or X‐ray. The automated classification of lung cancer is difficult due to the involvement of multiple steps in imaging patients' lungs. In this manuscript, human lung cancer classification and comprehensive analysis using different machine learning techniques is proposed. Initially, the input images are gathered using lung cancer dataset. The proposed method processes these images using image‐processing techniques, and further machine learning techniques are utilized for categorization. Seven different classifiers including the k‐nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), multinomial naive Bayes (MNB), stochastic gradient descent (SGD), random forest (RF), and multi‐layer perceptron (MLP) classifier are used, which classifies the lung cancer as malignant and benign. The performance of the proposed approach is examined using performances metrics, like positive predictive value, accuracy, sensitivity, and f‐score are evaluated. Among them, the performance of the MLP classifier provides 25.34%, 45.39%, 15.39%, 41.28%, 22.17%, and 12.12% higher accuracy than other KNN, SVM, DT, MNB, SGD, and RF respectively. Research Highlights Lung cancer is a leading cause of cancer‐related death. Imaging (MRI, CT, and X‐ray) aids diagnosis. Automated classification of lung cancer faces challenges due to complex imaging steps. This study proposes human lung cancer classification using diverse machine learning techniques. Input images from lung cancer dataset undergo image processing and machine learning. Classifiers like k‐nearest neighbors, support vector machine, decision tree, multinomial naive Bayes, stochastic gradient descent, random forest, and multi‐layer perceptron (MLP) classify cancer types; MLP excels in accuracy. In this manuscript, human lung cancer classification and comprehensive analysis using different machine learning techniques is proposed. Initially, the input images are gathered using lung cancer dataset. The proposed method processes these images using image‐processing techniques, and further machine learning techniques are utilized for categorization. Seven different classifiers including the k‐nearest neighbors (KNN), support vector machine (SVM), decision tree (DT), multinomial naive Bayes (MNB), stochastic gradient desc