## 1 Introduction
### Related Works  
Various approaches have been proposed in the literature for medical imaging CAD systems, including those for malaria, DR and TB [27; 28; 29; 30; 31; 32; 33; 34; 35; 36; 37; 38; 39; 40; 41; 42; 43]. The work described in [28] aims to improve malaria parasite detection using tiny red blood smear patches; they utilize several existing deep convolutional neural networks in place of handcrafted feature extraction. The study claims that using preprocessing techniques such as standardization, normalization, and stain normalization does not improve the overall performance model. An effective multi-magnification deep residual neural network (MM-ResNet) has been trained on microscopic phone image datasets of malaria blood smears obtained from the AI research group at Makerere University. The MM-ResNet-50 end-to-end framework takes three different images of size as inputs. It concatenates each ResNet-50 at the second to the last layer, followed by a final fully connected layer [29]. VGG-16 and VGG-19 have been trained on the National Institutes of Health (NIH) malaria dataset using hyperparameter tuning techniques described in [30], the CNN used to automate the screening of malaria in low-resource countries achieves an accuracy of 0.9600. A survey article on image analysis of microscopic blood slides uses many machine learning techniques for malaria detection [31]. Patient information was considered, such as nationality, age, gender, body region, and symptomatology of a patient as a part of features engineering for malaria detection. Furthermore, they examined six machine learning algorithms, including support vector machine (SVM), random forest (RF), multilayer perceptron (MLP), AdaBoost, gradient boosting (GB), and CatBoost to classify infected and non-infected cells [32]. A fast CNN architecture present in [33] is used to classify thin blood smeray images. This paper studied the performance of transfer learning approaches for various pre-trained CNN architectures, including AlexNet, ResNet-50, VGG-16, and DenseNet-201. Furthermore, they studied the performance of a traditional machine learning algorithm using a bag-of-features model with SVM. Many deep learning methods, originally proposed for the ILSVRC [44], have been adapted to the medical image application. Among these are meta-algorithms for DR detection, which combine five CNN architectures into one predictive model [34]. [PERSON] et al. [35] fine-tuned ResNet-50 that pre-trained on the ImageNet dataset. The work described in [36] developed a real-time smartphone app to detect and classify DR by using a pre-trained Inception v3 model with a transfer learning technique. A hybrid machine learning technique is introduced in [37] to detect and grade DR severity level. The study compares simple transfer learning-based approaches using seven pre-trained networks. Another fine-tuned, pre-trained approach for DR detection is presented in [38] using a cosine annealing strategy to decay the learning rate. The transfer learning method for TB described in [39] was used to neutrophil cluster detection. An automatic TB screening system presented in [40] is based on transfer learning from lower convolutional layers of pre-trained networks. The method in [42] uses a simple segmentation approach to classify the images' foreground and background. The segmented objects are then fed to a trained CNN to classify the objects into bacilli and non-bacilli. A total of four state-of-the-art 3D CNN models are used to detect the spatial location of lesions and classify the candidates into miliary, infiltrative, caseous, tuberculoma, and cavitary types in [43]. A multi-strategy fast non-dominated solution ranking algorithm with high robustness is described in [45]. Of particular relevance to our work is the Net2 Net method introduced by [PERSON] et al. [46] The method is modular in that it allows two neural networks to mimic the behavior of a more complex network.