Classification of Diabetes and Cardiac Arrhythmia using Deep Learning

Master's thesis Information- and communication technology IKT591 - University of Agder 2018Deep Learning (DL) is a research area that has ourished signi cantly in the recent years and has shown remarkable potential for arti cial intelligence in the eld of medical applications. The reasons for success are the ability of DL algorithms to model high-level abstractions in the data by using automatic feature extraction property as well as signi cant amount of medical data that is available for training these algorithms. DL algorithms can learn features from a large volume of healthcare data, and then use the procured insights to assist clinical practice. We have implement DL algorithm for the classi cation of two diseases in the medical domain: Diabetes and Cardiac Arrhythmia. Diabetes is often considered as one of the world's major health problems according to the World Health Organization. Recent surveys indicate that there is an increase in the number of diabetic patients resulting in the increase in serious complications such as heart attacks and deaths. This thesis presents a Multi-Layer Feed Forward Neural Networks (MLFNN) for the classi cation of diabetes on publicly available Pima Indian Diabetes (PID) dataset. A series of experiments are conducted on this dataset with variation in learning algorithms, activation units, techniques to handle missing data and their impact on classi cation accuracy have been discussed. Finally, the results are compared with other machine learning algorithms like Na ve Bayes, Random Forest, and Logistic Regression. The achieved classi cation accuracy by MLFNN (82.5%) is the best of all the other classi ers. The term arrhythmia refers to any variation in the usual sequence of the heartbeat. There are many types of cardiac arrhythmia ranging in severity, including Premature Atrial Contractions (PACs), Atrial Fibrillation, and Premature Ventricular Contractions (PVCs). This thesis focuses on the use of DL algorithms: Convolutional Neural Network (CNN) and Long Short- Term Memory (LSTM) to classify arrhythmia with minimum possible data pre-processing on MIT-BIH Arrhythmia Database (MIT dataset). Furthermore, we study the in uence of di erent hyperparameters like L2 regularization and number of epochs on the classi cation accuracy of LSTM. We achieved a classi cation accuracy of 99.19% and 98.40% with CNN and LSTM models respectively. From our research, we believe that CNN model can assist the doctors in the classi cation of arrhythmia

SleepXAI: An explainable deep learning approach for multi-class sleep stage identification

Extensive research has been conducted on the automatic classification of sleep stages utilizing deep neural networks and other neurophysiological markers. However, for sleep specialists to employ models as an assistive solution, it is necessary to comprehend how the models arrive at a particular outcome, necessitating the explainability of these models. This work proposes an explainable unified CNN-CRF approach (SleepXAI) for multi-class sleep stage classification designed explicitly for univariate time-series signals using modified gradient-weighted class activation mapping (Grad-CAM). The proposed approach significantly increases the overall accuracy of sleep stage classification while demonstrating the explainability of the multi-class labeling of univariate EEG signals, highlighting the parts of the signals emphasized most in predicting sleep stages. We extensively evaluated our approach to the sleep-EDF dataset, and it demonstrates the highest overall accuracy of 86.8% in identifying five sleep stage classes. More importantly, we achieved the highest accuracy when classifying the crucial sleep stage N1 with the lowest number of instances, outperforming the state-of-the-art machine learning approaches by 16.3%. These results motivate us to adopt the proposed approach in clinical practice as an aid to sleep experts.publishedVersionPaid Open Acces

Statistical Region Based Segmentation of Ultrasound Images

Segmentation of ultrasound images is a challenging problem due to speckle, which corrupts the image and can result in weak or missing image boundaries, poor signal to noise ratio, and diminished contrast resolution. Speckle is a random interference pattern that is characterized by an asymmetric distribution as well as significant spatial correla- tion. These attributes of speckle are challenging to model in a segmentation approach, so many previous ultrasound segmentation methods simplify the problem by assuming that the speckle is white and/or Gaussian distributed. Unlike these methods, in this paper we present an ultrasound-specific segmentation approach that addresses both the spatial correlation of the data as well as its intensity distribution. We first decorrelate the image and then apply a region-based active contour whose motion is derived from an appropri- ate parametric distribution for maximum likelihood image segmentation. We consider zero-mean complex Gaussian, Rayleigh, and Fisher-Tippett flows, which are designed to model fully formed speckle in the in-phase/quadrature (IQ), envelope detected, and display (log compressed) images, respectively. We present experimental results demon- strating the effectiveness of our method, and compare the results to other parametric and non-parametric active contours

‘May God Give Us Chaos, So That We Can Plunder’: A critique of ‘resource curse’ and conflict theories

Kuntala Lahiri-Dutt scrutinizes the increasingly popular theories of the natural resources curse, natural resource conflicts and natural resource wars. She argues that we need to rethink the issues around resource ownership rights as well as the legal frameworks governing and controlling ownership of the mineral-rich tracts of developing countries. Based on her activist research with mining communities she shows that mineral resource management is characterized by multiple actors with their multiple voices, and it is important for us to recognize these actors and listen to their voices. Development (2006) 49, 14–21. doi:10.1057/palgrave.development.1100268