Unveiling biological significance deep learning methods for lung cancer segmentation and classification in the context of environmental protection and bionetwork

Abstract

Lung carcinoma is a malignant condition that develops in the newlinetissues of the lungs. Due to its propensity for early metastasis, lung cancer is newlinevery lethal and poses significant challenges in terms of treatment. Lung newlinecancer has consistently held the position of being the most prevalent form of newlinecancer worldwide for many years, as stated by the World Health Organization newline(WHO). Early detection and treatment of lung cancer are crucial. These newlineoutcomes may result in more treatment alternatives, reduced need for newlineintrusive procedures, and improved chances of survival. Cancer often newlineoriginates in the lungs or metastasizes to the lungs. Primary lung cancer refers newlineto instances that begin in the lungs, whereas secondary lung cancer, also newlineknown as lung metastasis, refers to cases that spread from another region of newlinethe body to the lungs. newlineTypically, lung cancer mostly denotes primary lung cancer. newlinePrimary lung cancer is categorized into two primary types: Non-Small Cell newlineLung Cancer (NSCLC) and Small Cell Lung Cancer (SCLC). Medical newlineimaging is crucial in modern medical practices, including diagnosis, newlinetreatment, and surgery. Imaging devices like Computed Tomography (CT), newlineMagnetic Resonance Imaging (MRI), and ultrasound diagnostics, along with newlineComputer Aided Diagnosis (CAD) systems, provide valuable information newlineabout diseases and organs. The first phase of this study presents a method for newlineeffectively segmenting and classifying lung cancer. This is achieved via the newlineuse of U-net segmentation and Deep Convolution Neural Network (DCNN), newlinewhich enables the detection of malignant tumors. newline