Department of Computer Sciencehttps://karuspace.karu.ac.ke/handle/20.500.12092/16482024-03-29T05:20:03Z2024-03-29T05:20:03ZDeep Transfer Learning Optimization Techniques for Medical Image Classification: A ReviewKariuki, Paul WahomeGikunda, Patrick KinyuaWandeto, John Mwangihttps://karuspace.karu.ac.ke/handle/20.500.12092/30312024-01-11T00:00:22Z2022-01-01T00:00:00ZDeep Transfer Learning Optimization Techniques for Medical Image Classification: A Review
Kariuki, Paul Wahome; Gikunda, Patrick Kinyua; Wandeto, John Mwangi
Medical image classification is a complex and challenging task due to the heterogeneous nature of medical data. Deep transfer learning has emerged as a promising technique for medical image classification, allowing the leveraging of knowledge from pre-trained models learned from large-scale datasets, resulting in improved performance with minimal training and overcoming the disadvantage of small data sets. This paper concisely overviews cutting-edge deep transfer learning optimization approaches for medical image classification. The study covers convolutional neural networks and transfer learning techniques, including relation-based, feature-based, parameter-based, and instance-based transfer learning. Classical classifiers such as Resnet, VGG, Alexnet, Googlenet, and Inception are examined, and their performance on medical image classification tasks is compared. The paper also discusses optimization techniques, such as batch normalization, regularization, and weight initialization, as well as data augmentation and kernel mathematical formulations. The study concludes by identifying challenges when using deep transfer learning for medical image classification and proposing potential future approaches for this field.
Deep transfer learning optimization techniques for medical image classification
2022-01-01T00:00:00ZFactors Inhibiting the Implementation of Digital Villages in KenyaKarume, SimonShisoka, Dorcus Arshleyhttps://karuspace.karu.ac.ke/handle/20.500.12092/23122019-10-15T00:00:16Z2017-01-01T00:00:00ZFactors Inhibiting the Implementation of Digital Villages in Kenya
Karume, Simon; Shisoka, Dorcus Arshley
The achievement of an information-based society is one of the main priorities of the Government of Kenya (GoK)
towards the realization of national development goals and objectives for wealth and employment creation. However,
even in their efforts the ICT sector is still currently more active in urban areas, resulting in wide regional disparities
in the distribution of ICT facilities. In order to address this disparity, the Kenya ICT Board (KICTB) supported the
roll out of new “electronic centre’s” which were named Pasha Centre’s (and are also commonly referred to as Digital
Villages).The Digital village’s initiatives in Kenya commenced with a lot of optimism in 2009 however five years
down the line it cannot be recorded that they have been successful. The purpose of this study was to establish the
factors that have hindered the successful implementation of digital villages in Kenya. For this study desk research
methodology was adopted. The secondary data from published reports was discussed with emphasis on the area of
interest to this study. The findings of this study indicated that there were various factors that hindered the successful
implementation of digital villages in Kenya. The study recommended need for having a government policy for the
digital village project. This policy if developed will serve to protect such projects in future enabling them to take off
and function independently.
2017-01-01T00:00:00Z