Faculty Publications
Permanent URI for this communityhttps://idr.nitk.ac.in/handle/123456789/18736
Publications by NITK Faculty
Browse
Search Results
Item A novel Deep Learning architecture for lung cancer detection and diagnosis from Computed Tomography image analysis(Elsevier Inc., 2024) Crasta, L.J.; Neema, R.; Pais, A.R.Timely identification of lung nodules, which are precursors to lung cancer, and their evaluation can significantly reduce the incidence rate. Computed Tomography (CT) is the primary technique used for lung cancer screening due to its high resolution. Identifying white, spherical shadows as lung nodules in CT images is essential for accurately detecting lung cancer. Convolutional Neural Network (CNN)-based methods have performed better than traditional techniques in various medical image applications. However, challenges still need to be addressed due to insufficient annotated datasets, significant intra-class variations, and substantial inter-class similarities, which hinder their practical use. Manually labeling the position of nodules on CT slices is critical for distinguishing between benign and malignant cases, but it is an unreliable and time-consuming process. Insufficient data and class imbalance are the primary factors that may result in overfitting and below-par performance. The paper presents a novel Deep Learning (DL) framework to detect and classify lung cancer in input CT images. It introduces a 3D-VNet architecture for accurate segmentation of pulmonary nodules and a 3D-ResNet architecture designed for their classification. The segmentation model achieves a Dice Similarity Coefficient (DSC) of 99.34% on the LUNA16 dataset while reducing false positives to 0.4%. The classification model shows performance metrics with accuracy, sensitivity, and specificity of 99.2%, 98.8%, and 99.6%, respectively. The 3D-VNet network outperforms previous segmentation methods by accurately calibrating lung nodules of various sizes and shapes with excellent robustness. The classification model's metrics show that the suggested method outperforms current approaches regarding accuracy, specificity, sensitivity and F1-Score. © 2024 The AuthorsItem Transfer Learning-Hierarchical Segmentation on COVID CT Scans(Springer, 2024) Singh, S.; Pais, A.R.; Crasta, L.J.COVID-19—A pandemic declared by WHO in 2019 has spread worldwide, leading to many infections and deaths. The disease is fatal, and the patient develops symptoms within 14 days of the window. Diagnosis based on CT scans involves rapid and accurate detection of symptoms, and much work has already been done on segmenting infections in CT scans. However, the existing work on infection segmentation must be more efficient to segment the infection area. Therefore, this work proposes an automatic Deep Learning based model using Transfer Learning and Hierarchical techniques to segment COVID-19 infections. The proposed architecture, Transfer Learning with Hierarchical Segmentation Network (TLH-Net), comprises two encoder–decoder architectures connected in series. The encoder–decoder architecture is similar to the U-Net except for the modified 2D convolutional block, attention block and spectral pooling. In TLH-Net, the first part segments the lung contour from the CT scan slices, and the second part generates the infection mask from the lung contour maps. The model trains with the loss function TV_bin, penalizing False-Negative and False-Positive predictions. The model achieves a Dice Coefficient of 98.87% for Lung Segmentation and 86% for Infection Segmentation. The model was also tested with the unseen dataset and has achieved a 56% Dice value. © The Author(s), under exclusive licence to The Japanese Society for Artificial Intelligence and Springer Nature Japan KK, part of Springer Nature 2024.