DrugPal: A Machine Learning Based Drug Recommender System To Assist Physician

Bhattacharya, Samayan; Bhattacharya, Avigyan; Sk, Shahnawaz,; Islam, Asraful

doi:10.1109/sces55490.2022.9887760

Cited by 2 publications

(2 citation statements)

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“…Medical images from diverse sources, such as computed tomography (CT) and magnetic resonance imaging (MRI), contrastive learning is used in this study to train Unet++ for semantic segmentation of medical images without the requirement for pixel‐by‐pixel annotations 43 . Here, we go over the structure of the suggested model and the training strategy.…”

Section: Related Workmentioning

confidence: 99%

“…25 Medical images from diverse sources, such as computed tomography (CT) and magnetic resonance imaging (MRI), contrastive learning is used in this study to train Unet++ for semantic segmentation of medical images without the requirement for pixel-by-pixel annotations. 43 Here, we go over the structure of the suggested model and the training strategy. A brand-new target tracking technique based on correlation filters is put out by Yang et al 44 This technique makes use of the enhanced Tracking-Learning-Detection (TLD) tracking framework, which connects the tracker and detector via the learning process.…”

Section: Related Workmentioning

confidence: 99%

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Microaneurysm classification system in color fundus images using auto‐weight dilated convolutional network

Jayachandran,

Ratheesh Kumar

2023

Int J Imaging Syst Tech

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Microaneurysms (MAs) are the early indications of diabetic retinopathy (DR), which may result in total visual loss. MAs detection is an exciting work due to its small, darkish color and subtle nature. The automatic detection and categorization of MAs in retinal fundus images using a multi‐scale approach based on Deep Neural Networks and Neighborhood Analysis is proposed in this research article. MAs segmentation, classification, and preprocessing comprise the three steps of the proposed technique. To extract the multi‐scale MA features, the auto‐weight dilated convolutional unit (AD) is specifically used for convolutional feature maps. To fuse convolutional feature maps in encoding layers, the AD unit used a learnable set of parameters. An efficient architecture for feature extraction during the encoding step is incorporated into the AD unit. We integrated the AD unit with the benefits of the U‐Net network for deep and shallow features. Additionally, in order to improve the suggested model and produce the final derivation, we developed a novel optimization approach. After that, neighborhood analysis is performed to name the Micro‐aneurysm because the lesion is actually a collection of independent little images rather than the entire image. The classification accuracy of the proposed method for the three different data sets such as MESSIDOR, online retinal fundus image database for glaucoma analysis (ORIGA), and RIM‐ONE‐R1, is 99.28%, 98.95%, and 98.76% respectively. The results show a good performance of the proposed model against the other analyzed procedure.

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Section: Related Workmentioning

confidence: 99%