Conference Papers
Permanent URI for this collectionhttps://idr.nitk.ac.in/handle/123456789/28506
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Item M-CAD: Towards Multi-Categorical Auto Diagnosis of Varied Diseases using Deep Learning(Institute of Electrical and Electronics Engineers Inc., 2021) Praveen, K.; Patil, N.; Srikanth, C.S.; Nayaka, J.The economic burden and the number of lives lost due to diagnostic errors are higher than ever due to the onset of pandemics and new viruses, Specially in medium and low-economic status nations (including India) are affected heavily in terms of capital and human resources. Due to limited expertise in diagnostic technologies in remote parts of India and many low-economic nations of Africa, autonomous diagnostics can save millions of lives and lower the costs. To accomplish this goal we propose a method that uses modern developments in Deep Learning in semantic segmentation and classification to predict multiple diseases from multiple medical images. To conduct the study we test the model with Dermoscopy images and CT-Scans to predict 8 classes relating to Melanoma cancer, Covid-19 virus and different types of Carcinoma. The setup is tested on largest publicly available ISIC Dermoscopy dataset, 1061 CT-scan images combined for the classification and Segmentation(only for Melanoma). Classification model(M-CAD) is progressively tested by increasing the number of classes and data that it trains on. This pilot study is conducted on a small subset of the complete data, segmentation of Melanoma images obtained an accuracy of 96.6% compared to human expert agreement which is 90.9%. we were able to produce average accuracy of 81.5% and AUC of 0.94 for 6 classes using CT-Scans whereas accuracy and AUC for all the 8 classes is 80.2% and 0.97 respectively. These results were quite promising for a model that classifies different images with no apparent relation at all. © 2021 IEEE.Item From Pixels to Prognosis: Exploring from UNet to Segment Anything in Mammogram Image Processing for Tumor Segmentation(Institute of Electrical and Electronics Engineers Inc., 2024) Hithesh, M.R.; Vishwanath, V.K.Breast cancer, is the most prevalent form of cancer among women globally accounting for nearly one in four of all new cancer cases. This high prevalence makes breast cancer the second leading cause of death among women making early detection of breast cancer crucial for reducing mortality rates. Mammography, a widely employed medical imaging technique, emerges as a front-line defense against breast cancer. The research aims to achieve pivotal objectives: Firstly, to examine the Segment Anything Model (SAM), created by MetaAI in 2023, and also discuss the model's use in medical image segmentation. Secondly, the study aims to conduct a comprehensive comparative analysis of SAM's segmentation capabilities with other existing segmentation models for medical images. The unique strength of the research lies in incorporating both mass and calcification mammograms within the diverse Curated Breast Imaging Subset of DDSM (CBIS-DDSM) dataset this acknowledges the varied nature of breast cancer, exposing the segmentation models to a wide range of scenarios. Through augmenting the dataset with vertical, horizontal, and rotational transformations, this enables accurate identification and isolation of Region of Interests (RoIs) in mammograms, for robust model training for real-world breast cancer diagnosis. The study investigates different segmentation models for medical image segmentation, with an emphasis on prompt-based (SAM), Encoder-Decoder (UNet, UNet++, Attention-UNet, SegNet) models. The study extends to evaluating these models based on Key Performance Indicators (KPIs) such as Dice score, Intersection over Union (IoU) and Accuracy. The insights gained from this research have broader implications for the application of more accurate segmentation in medical image analysis, and making a significant contribution to the continuous efforts to improve breast cancer diagnostic methodologies. © 2024 IEEE.