Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Towards non-vascular fundus image analysis and disease detection

Assessment of retinal fundus image is very informative and preventive in early ocular disease detection. This non-invasive assessment of fundus images also helps in the early diagnosis of vascular diseases. This unique combination help in the early diagnosis of diseases. Applying image enhancement techniques with advanced Deep learning techniques helps to overcome such a challenging problem. Most Deep learning models give a diagnosis without attention to underlying pathological abnormalities. In this thesis, we tried to solve the problem in the same way as ophthalmologists and experts in the field approach the problem. We created models that can detect an Optic disc, Optic cup, and vascular regions in the image. This work can be integrated into any ocular disease detection, such as glaucoma, and vascular disease detection, such as diabetes. Extensive work is applied for better sampling when all models were suffering from a lack of data in the medical imaging field. The entire work on the retinal fundus image was in 2d images. In the extension of this work, we applied our knowledge to 3d MRI-Brain images. We attempt to predict attention scores in children, which is a big factor in the detection of kids with ADHD. But both work on fundus images and brain MRI images are under the umbrella of medical imaging. We believe this advancement in this line of research can be very valuable for future researchers in the area of automated medical imaging, especially in automated retinal disease diagnosis

LMBiS-Net: A Lightweight Multipath Bidirectional Skip Connection based CNN for Retinal Blood Vessel Segmentation

Blinding eye diseases are often correlated with altered retinal morphology, which can be clinically identified by segmenting retinal structures in fundus images. However, current methodologies often fall short in accurately segmenting delicate vessels. Although deep learning has shown promise in medical image segmentation, its reliance on repeated convolution and pooling operations can hinder the representation of edge information, ultimately limiting overall segmentation accuracy. In this paper, we propose a lightweight pixel-level CNN named LMBiS-Net for the segmentation of retinal vessels with an exceptionally low number of learnable parameters \textbf{(only 0.172 M)}. The network used multipath feature extraction blocks and incorporates bidirectional skip connections for the information flow between the encoder and decoder. Additionally, we have optimized the efficiency of the model by carefully selecting the number of filters to avoid filter overlap. This optimization significantly reduces training time and enhances computational efficiency. To assess the robustness and generalizability of LMBiS-Net, we performed comprehensive evaluations on various aspects of retinal images. Specifically, the model was subjected to rigorous tests to accurately segment retinal vessels, which play a vital role in ophthalmological diagnosis and treatment. By focusing on the retinal blood vessels, we were able to thoroughly analyze the performance and effectiveness of the LMBiS-Net model. The results of our tests demonstrate that LMBiS-Net is not only robust and generalizable but also capable of maintaining high levels of segmentation accuracy. These characteristics highlight the potential of LMBiS-Net as an efficient tool for high-speed and accurate segmentation of retinal images in various clinical applications