ADVANCED AI-DRIVEN FRAMEWORK FOR LUNG CANCER DETECTION USING CT SCAN IMAGES AND MOBILE APPLICATION INTEGRATION

Аннотация

Lung cancer is among the leading causes of cancer-related deaths globally, primarily due to late-stage detection. Recent advancements in Artificial Intelligence (AI) and deep learning have opened new pathways for early and accurate diagnosis of lung cancer, addressing the limitations of traditional diagnostic methods. This study introduces an enhanced approach for processing lung CT (Computed Tomography) scan images using deep convolutional neural networks (CNNs) integrated with advanced noise reduction and feature extraction techniques. Unlike prior studies, our model dynamically applies adaptive thresholding using k-means clustering and morphological operations to isolate the lung region effectively and identify cancerous nodules. Additionally, we integrate 3D image meshing for improved visualization and analysis. To bridge the gap between diagnosis and patient awareness, the proposed model is supported by a mobile application ecosystem, enabling real-time health monitoring and communication. This research further emphasizes the potential of AI-based methodologies in detecting lung cancer at earlier stages, thereby reducing mortality rates. The paper also highlights directions for future advancements, such as staging cancerous tissues and improving diagnostic precision through more robust training datasets. Keywords: Lung Cancer, Artificial Intelligence, Deep Learning, CNN, CT Scan, Mobile Health Applications. 1. INTRODUCTION: Lung cancer remains one of the most challenging and fatal diseases worldwide, accounting for a significant proportion of cancer-related deaths annually. Its insidious progression and delayed onset of symptoms often result in diagnosis at advanced stages, where treatment options are limited, and survival rates drastically decrease. Lung cancer is primarily categorized into two types: small cell lung cancer (SCLC) and non-small cell lung cancer (NSCLC), with NSCLC being the most prevalent, constituting 80–85% of cases. Early detection of cancerous nodules within lung tissues is critical for effective treatment and improved patient outcomes. The advent of Artificial Intelligence (AI) and advancements in digital imaging techniques have revolutionized medical diagnostics, offering new tools for more 39 7 https://universalpublishings.com МЕДИЦИНА, ПЕДАГОГИКА И ТЕХНОЛОГИЯ: ТЕОРИЯ И ПРАКТИКА Researchbib Impact factor: 11.79/2023 SJIF 2024 = 5.444 Том 2, Выпуск 11, Ноябрь accurate and efficient detection of diseases. Traditional diagnostic approaches in medical imaging often rely on manual interpretation and handcrafted features, which can be prone to variability and error. Recent developments in deep learning, particularly convolutional neural networks (CNNs), provide a robust alternative by enabling automated feature extraction, classification, and prediction with high precision. This research builds on prior efforts by proposing a comprehensive AI-powered system for lung cancer detection using CT scan images. Our approach incorporates advanced preprocessing techniques, including noise removal, adaptive segmentation, and dynamic thresholding, to enhance the quality of medical images for analysis. The integration of CNNs facilitates the detection of cancerous nodules, while 3D meshing and visualization improve diagnostic clarity. In addition to imaging advancements, this study also emphasizes patient-centric care through mobile applications. These applications not only serve as tools for diagnosis and monitoring but also play a pivotal role in patient education, communication, and mental health support. By bridging the gap between AI-driven diagnostics and patient interaction, we aim to create a holistic solution for managing lung cancer. The remainder of this paper discusses the related work, the proposed methodology, experimental results, and potential areas for future research. We highlight the significance of AI-based innovations in reducing diagnostic delays and mortality rates associated with lung cancer. 2. RELATED WORK: Lung cancer detection has been a focal area of research in medical imaging due to its high mortality rate and the complexities associated with its early diagnosis. Traditional methods rely on handcrafted features and rule-based systems, which often lack the precision and scalability required for large datasets. With advancements in artificial intelligence (AI), researchers have shifted towards automated systems, leveraging machine learning (ML) and deep learning (DL) techniques to improve detection accuracy and efficiency. Several studies have demonstrated the potential of AI in medical imaging. For instance, early applications of image processing techniques utilized pixel-based filtering methods, such as Gaussian and median filters, to enhance image quality and reduce noise. These approaches were combined with segmentation algorithms, such as watershed and thresholding methods, to isolate regions of interest (ROIs) in CT images. While effective in reducing visual cl