## 5 Conclusions and Discussion  
In this research, we have proposed IMNS as a new method for designing and training deep learning models. The resulting networks are referred to as IMNets. We have demonstrated the efficacy of the proposed method in detecting three diseases using three different imaging modalities. The best performing IMNets in our study achieved a balanced accuracy of 97.0%, 97.9%, and 88.6% and AUC of 0.995, 0.996, and 0.949 for the detection of malaria, DR, TB, respectively. Our modular approach starts with a single SubNet and we add one additional SubNet at a time, either in series or in parallel with the previous network. Only the new SubNet weights are updated at each stage of IMNS. This approach keeps the computational complexity low and allows the network to train well with a relatively small training set. The performance of IMNets rivals, and in some cases exceeds, that of much larger state-of-the-art networks where transfer learning is employed. We attribute this to the relatively small training sets available and the limitations of transfer learning. Since the pre-trained networks are trained for a different application, significant adaption may be required for a new task. Large networks can be very powerful where there are sufficient data to properly train them. However, the large networks, with a high number of learnable parameters, can become a liability when only small training sets are available. In other words, for large pre-trained models to be helpful, both extensive data from the same domain and large computational resources are required. It remains the case that large truthed datasets for medical imaging applications are often difficult to come by. This behoves us to explore more compact networks and training strategies such as the proposed IMNS. Monolithic deep learning with transfer learning may suffer from overfitting issues, due to limited training data in many medical image analysis applications. In addition, the computational cost grows with deeper and wider monolithic networks. The building-block IMNS approach addresses these issues by employing relatively small SubNets and training only one SubNet at a time. As we can see in the results section, our IMNS provides results that rival or exceed many popular large-scale models in the experiments presented here. Moreover, our IMNets trained faster, had lower memory requirements, and processed test images more quickly than the benchmark methods tested. From a learning perspective, we believe IMNS has several benefits over monolithic deep learning. As with other modular approaches, complex problems are addressed using several small SubNets, rather than one large monolithic network. We believe this helps to mitigate the complex optimization difficulties and vanishing gradient problems that monolithic CNN approaches face. Furthermore, our results suggest that the IMNS allows for the effective transfer of prior knowledge from the fixed portion of the IMNet to a new SubNet. In future work, we plan to extend the architecture of IMNets in two ways. First, we will investigate the impact of combining these SubNets in different configurations. Moreover, we will also examine different SubNet architectures. Conceptualization, R.A. and R.C.H. ; methodology, R.A. and R.C.H. ; software, R.A. and R.C.H. ; validation, R.A., R.C.H. and B.N.N. ; writing--original draft preparation, R.A.; writing--review and editing, R.A., R.C.H., B.N.N. and T.M.K. ; supervision, R.C.H., B.N.N. and T.M.K. All authors have read and agreed to the published version of the manuscript. This research received no external funding. This research received no external funding. Not applicable. Not applicable. Not applicable.