1. Type 1 diabetes (due to b-cell destruction, usually leading to absolute insulin deficiency) 2. Type 2 diabetes (due to a progressive insulin secretory defect on the background of insulin resistance) 3. Gestational diabetes mellitus (GDM) (diabetes diagnosed in the second or third trimester of pregnancy that is not clearly overt diabetes) 4. Specific types of diabetes due to other causes, e.g., monogenic diabetes syndromes (such as neonatal diabetes and maturity-onset diabetes of the young [MODY]), diseases of the exocrine pancreas (such as cystic fibrosis), and drugor chemical-induced diabetes (such as in the treatment of HIV/AIDS or after organ transplantation)

Classification and diagnosis of diabetes

Hyperglycemia and Adverse Pregnancy Outcomes

A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification

U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

A survey on ECG analysis

Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains

A time-frequency domain approach of heart rate estimation from photoplethysmographic (PPG) signal

Of late, usage of neural network in the field of disease detection has been on advanced stage. Hence, ocular disease diagnosis has also been under the influence of machine learning. Human eye is very prone to disorders like cataract, glaucoma, myopia etc. and with the passage of time, these diseases get more and more complex and the vision of human eye gradually decays. As a matter of fact, their early detection is mandatory in order to preclude complete blindness. Several diagnosis tests like visual acuity test, retinal exam, ocular tonometry are undertaken in real life but these are undoubtedly time consuming and frustrating for the patient as well. In this paper, a unique method for detecting eight types of ocular diseases using convolutional neural network (CNN) has been presented and its performance is evaluated. The affected regions for some diseases can also be detected. Some conventional pre-processing are performed and the data is sent to the network for rigorous classification. The model has been trained and tested with high-end graphics processing unit (GPU) on a brand-new dataset. Our developed model has achieved a cogent F-score of approx. 85%, Kappa score of 31% and an AUC value of 80.5%. Since this is the first “real-life” (i.e. plausible for a clinical scenario of patients including camera variation) prediction of multiple diseases in an eye based on this dataset, there is hardly any analogous task to compare with. So, our model has also been performed on other dataset and it has excelled with convincing F-score, Kappa score and an AUC value.

Source and Camera Independent Ophthalmic Disease Recognition from Fundus Image Using Neural Network

Myocardial Infarction is one of the leading causes of death worldwide. This paper presents a Convolutional Neural Network (CNN) architecture which takes raw Electrocardiography (ECG) signal from lead II, III and AVF and differentiates between inferior myocardial infarction (IMI) and healthy signals. The performance of the model is evaluated on IMI and healthy signals obtained from Physikalisch-Technische Bundesanstalt (PTB) database. A subject-oriented approach is taken to comprehend the generalization capability of the model and compared with the current state of the art. In a subject-oriented approach, the network is tested on one patient and trained on rest of the patients. Our model achieved a superior metrics scores (accuracy= 84.54%, sensitivity= 85.33% and specificity= 84.09%) when compared to the benchmark. We also analyzed the discriminating strength of the features extracted by the convolutional layers by means of geometric separability index and euclidean distance and compared it with the benchmark model.

Celia Shahnaz

Papers

Denoising of ECG signals based on noise reduction algorithms in EMD and wavelet domains

A time-frequency domain approach of heart rate estimation from photoplethysmographic (PPG) signal

Source and Camera Independent Ophthalmic Disease Recognition from Fundus Image Using Neural Network

Detection of inferior myocardial infarction using shallow convolutional neural networks

Noisy speech enhancement based on an adaptive threshold and a modified hard thresholding function in wavelet packet domain