IMNets: Deep Learning Using an Incremental Modular Network Synthesis Approach for Medical Imaging Applications

Date of Award


Degree Name

Ph.D. Electrical and Computer Engineering


Department of Electrical and Computer Engineering


Russell C. Hardie


Purpose: To present and demonstrate a computationally efficient deep learning approach for computer-aided detection systems for medical imaging applications that include malaria, diabetic retinopathy, and tuberculosis. Approach: We propose a novel and a computationally efficient deep learning approach for medical image analysisusing convolutional neural networks (CNNs). We demonstrate the efficacy of our proposed method in the detection of malaria, diabetic retinopathy, and tuberculosis. We refer to our approach as Incremental Modular Network Synthesis (IMNS), and the resulting CNNs as Incremental Modular Networks (IMNets). Our IMNS approach is to use small network modules that we call SubNets that are capable of generating salient features for a particular problem. Then, we build up ever larger and more powerful networks by combining these SubNets in different configurations. At each stage, only one new SubNet module undergoes learning updates. This reduces the computational resource requirements for training and aids in network optimization. Results: We compare IMNets against classic and state-of-the-art deep learning architectures such as AlexNet, ResNet-50, Inception v3, DenseNet-201, and NasNet for the various experiments conducted in this study. Our proposed IMNS design leads to high average classification accuracies of 97.0%, 97.9%, and 88.6% for malaria, diabetic retinopathy, and tuberculosis, respectively. Conclusions: Our modular design for deep learning achieves the state-of-the-art performance in the scenarios tested. The IMNets produced here have a relatively low computational complexity compared to traditional deep learning architectures. The simpler IMNets train faster, have lower memory requirements, and process images faster than the benchmark methods tested


Electrical Engineering, Computer Engineering, Biomedical Engineering, Medical imaging, deep learning, malaria detection, diabetic retinopathy, tuberculosis detection, modular networks

Rights Statement

Copyright 2021, author.