Comprehensive Survey: Deep Classification of Rare and Accurate Disease using CT Image

Abstract

The accurate and early diagnosis of lung cancer and its pathological types is a significant task in the area of oncology, as the accuracy of the diagnosis has a direct effect on the treatment plan. The Computed Tomography (CT) imaging analysis is a key step in the evaluation of lungs; however, the analysis of images is generally affected by the variability of human analysis and the complex and heterogeneous nature of lung tissues. Lung cancer can be divided into two types: NonSmall Cell Lung Cancer (NSCLC) and Small Cell Lung Cancer (SCLC), which are biologically distinct and require completely different treatment plans. The inaccurate or late diagnosis often leads to the progression of cancer to an advanced stage, with a greater possibility of invasion of the adjacent organs and lower survival rates, thus highlighting the importance of early diagnosis and accurate characterization of lesions. Although the conventional radiomics-based approach offers a non-invasive approach to extract handcrafted texture and shape features from the CT images, the generalization performance of such approaches is generally limited. To address these challenges and approach, this research work proposes an artificial intelligence-assisted diagnostic method that employs deep learning-based volumetric analysis for effective feature representation and lesion localization and part. An improved three-dimensional convolutional neural network model is proposed using attention mechanisms, which is applied to efficiently analyze the spatial context and structural patterns of the pulmonary nodules texture. Deep features are extracted from the segmented tumor areas, which are then aggregated with radiomic features for efficient subtype prediction. In addition, uncertainty estimation and explainable techniques are applied to further improve the clinical credibility of the prediction results. By combining multi-organ segmentation, hierarchical feature learning, and probabilistic classification, the proposed method supports early diagnosis of lung cancer, subtype classification, and clinical decision-making. The combination of Al-assisted diagnostic systems has the potential to improve screening efficiency that enables personalized treatment planning, and further assist in improving survival rates in lung cancer treatment.