Browsing by Author "Dawit Assefa (PhD)"

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    A Trilingual Android Application with Automatic Malaria Detection from Microscopic Images of Red Blood Cells
    (Addis Ababa University, 2023-12) Berihun Nigussa; Dawit Assefa (PhD)
    Malaria, which is a mosquito-borne blood disease caused by Plasmodium parasites, is one of the virulent infectious diseases affecting human beings and other animals since antiquity. Even though there were promising progresses in the reduction of malaria morbidity and mortality in the past two decades before the outbreak of COVID-19, the latest two reports of theWorld Health Organization (WHO) statistics indicate that malaria has been overlooked due to the COVID pandemics. Malaria is still prevalent specifically in low resource setting areas such as the sub-Saharan African countries, including Ethiopia. WHO reported that there were 229 million new cases of malaria and 409,000 deaths globally in 2019, alone. Whereas in the year 2021, the morbidity and mortality was reported to rise up to 247 million and 619,000, respectively. Timely diagnosis and treatment as well as good awareness about the disease play a major role to combat malaria. In the current project work, it was intended to design and develop a multi-lingual Android App that offers useful information about the malaria disease and is capable of automatically detecting malaria infected red blood cells (RBCs) from color microscopic images based on a deep learning approach. The Convolutional Neural Network (CNN) based deep learning model was trained, validated and tested on a publicly available dataset composed of microscopic images of RBCs taken from individuals with confirmed malaria infection as well as normal control groups. Experimental results generated from the deep learning model showed that the detection capability of the model achieved 100% training accuracy, 96% validation accuracy and 96% testing accuracy. The developed App avails useful information about malaria disease in general and tips users with fundamental information regarding its prevention and transmission mechanisms acting as an m-health system.
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    Classification of Fatty Liver Disease Using Deep Learning Technique
    (Addis Ababa University, 2023-08) Hume Degebassa; Dawit Assefa (PhD); Million Molla (Mr.) Co-Advisor
    Fatty liver disease (FLD) also termed steatosis liver disease can be classified into two broad spectrums: Alcoholic fatty liver disease (AFLD) and Non-alcoholic liver disease (NAFLD). AFLD is a type of liver disease associated with alcohol intake of 40 to 80 grams of ethanol/day for males and of 20 to 40 grams/day for females. NAFLD is a disorder characterized by excess accumulation of fat (in the form of triglycerides) with an amount >5% in the hepatocytes. The progression of the disease is manifested by ongoing inflammation and consequent steatosis grade: G1 (mild steatosis), G2 (moderate steatosis), and G3 (severe steatosis). If no early strategies are adopted, FLD can progress to steatohepatitis (SH) which is a risk factor for liver fibrosis, cirrhosis, and hepatocellular carcinoma (liver cancer). Cirrhosis is currently the 11th most common cause of death globally and liver cancer is the 16th leading cause of death. In Ethiopia, cirrhosis was the 7th leading cause of mortality accounting for 24 deaths per 100,000 populations in 2019. Hence, early diagnosis is important to avoid advanced stages of FLD. In order to diagnose and grade FLDs, Ultrasound imaging is the most well-known technique. The visual assessment of these Ultrasound images is not only time taking and labor-intensive task but it is also prone to inter-observer variability. That calls for the development of an automated system that overcomes subjectivity and inconsistency in the screening process. Different studies in the literature have proposed the classification of FLDs using deep learning and machine learning techniques. However, most of these techniques are limited to binary classification and don’t consider the severity level. The current study aims to develop an automatic fatty liver classification and grading scheme using deep learning techniques. A total of 550 liver ultrasound images were collected from Zenode repository and image pre-processing was employed on the acquired images before the images were used to train the deep learning model. The effect of important deep network hyper parameters including batch size, learning rate, regularizer and epoch was investigated. Different pre-trained deep learning networks were tested for their classification efficacy including VGGNet, MobileNetV2, ResNet and Xception based on useful performance matrices. Accordingly, the Xception model was found to outperform the rest for multiclass (normal, mild, moderate, and severe) fatty liver classification. It offered a precision of 96.25%, recall of 97.75%, F1 score of 96.75%, and overall accuracy of 96.36% showing the great promises of the model.
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    Design and Development of Steam Sterilizer
    (Addis Ababa University, 2023-06) Yemane Tesfay; Dawit Assefa (PhD)
    According to the 2010 WHO infectious disease report, hundreds of millions of patients around the world are affected by healthcare-associated infections which cause extended clinic stays, long-term disability, massive additional costs, and unnecessary deaths. This folds higher in low-income countries but it is a global burden. To minimize the burden of these infections, cleaning, disinfection, and sterilization techniques have been applied. For a sterilization technique, different steam sterilizers are available in the market ranging from the simplest electro-mechanical portable systems to the complex microcontroller-based double-door floor fixed types. However, those steam sterilizers can’t adjust environmental pressure for altitudinal changes that often fail to function properly under significant pressure variation. Countries like Ethiopia have great geographical diversity, known altitude ranges from the highest peak at Ras Dashen (4,620m) down to the Dallol Depression (-148m), which highly affect the performance of steam sterilizers. The main intent of this thesis study is to design a microcontroller-based smart steam sterilizer that adjusts itself to variable altitudes since steam sterilizers that work in all altitudes are vital for the healthcare system. The sterilizer also comes with a water electrical conductivity sensor to completely avoid the use of hard water integrated with a water level regulator to protect the heater when water runs out inside the chamber. Interfacing between the proposed smart pressure compensator, conductivity sensor with water level regulator of steam sterilizers is worked out in this study. The developed control system senses the environmental condition using BMP280 sensor to adjust itself based on the altitudinal changes as well as water electrical conductivity, water level, pressure, and temperature sensors. A formula is developed based on the altitude pressure compensation to solve the problem of altitudinal effect and for water quality and quantity measurement guided by a programmable microcontroller. The control system can adjust the pressure based on the altitudinal variations. The system offers better efficiency, precision and comes with great ease of use. The designed smart steam sterilizer is as effective as existing modern sterilizers with an added feature of blocking use of hard water. Meanwhile, further research might be needed to make the device more efficient, lower cost, durable, and safer.
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    Improving Ultrasound Kidney Stone Detection Using Deep Learning
    (Addis Ababa University, 2023-04) Bekalu Gedifew; Dawit Assefa (PhD)
    Background: Currently a CT scan is preferred over Ultrasound images for Kidney stone diagnosis. The major problem regarding diagnosis of these stones under CT imaging mo-dality is that once these patients are diagnosed as positive; there is a high chance for the stone to be formed again in the patient’s lifetime after removal. As a result, use of the CT modality repeatedly exposes the patient for unwanted radiation exposure. Purpose of the Research: In ultrasound imaging, additive and multiplicative noises are taken as disadvantage for its imaging output. However, its ability to form real time imaging makes it preferable in many diagnosis procedures. The current study is aimed at developing an effective kidney stone detection scheme using a Convolutional Neural Network (CNN) by incorporating useful image pre-processing tools applied on Ultrasound images. Methods: The approach implemented in the proposed kidney stone detection scheme mainly involves two stages. The first stage employed using useful pre-processing steps applied on the Ultrasound images, which include image filtering, contrast enhancement and 2-D Directional Wavelet Transforms. The second stage is employed using multiclass se-mantic segmentation CNN models, which include Deep Lab V3 , U-net and Seg-net mod-els. In order to detect multiclass regions of ultrasound kidney stone image.The performance of the models was evaluated using useful quantitative matrices. Results and Conclusion: Results have shown that the Deep Lab V3 CNN model had greater performance than U-net and Seg-net CNN models tested in this study. The model was able to maintain a global accuracy and mean accuracy of 95.1% and 80.9% respec-tively showing its great promises in improving the detection of kidney stones based on Ultrasound images. Compared to performances reported in the literature by previous schol-ars who have developed different method of kidney stone detection algorithms, the pro-posed method has offered commendable results in terms of global accuracy and mean ac-curacy.