Fast and accurate diagnosis of illnesses or other health complications is not accessible in many locations around the world. Due to this, illnesses are unnecessarily left untreated. Therefore, AiDx Medical has developed a portable automated diagnostic microscope with re liable and rapid AI-assisted detection specifically for low-resource settings.

Scan speed is a key issue for the popularisation of whole slide imaging systems [1]. To address this issue a recent paper by [29] proposed a Kalman filter-based scanning algorithm. This approach eliminates the necessity for focus map generation prior to scanning and in doing so, reduces the scan time. Importantly, the proposed approach requires no additional hardware, and is more robust to noise.

In this thesis, two modifications to this work are proposed. Firstly, higher order process models are used to generate more precise estimates of the best-in-focus positions. Secondly, a two-dimensional Non-Symmetric Half Plane Kalman filter is developed to incorporate neighbouring state estimates in the prediction – an approach previously thought inapplicable for this purpose [24]. In a simulation, the new scanning algorithms are applied to scan thin smear malaria specimens and compared to state-of-the-art focus map surveying procedures.

AiDx Medical is a medical start-up, a spin-off from the TUD, which Temitope Agbana currently leads. AiDx is developing an automated diagnostic device for emerging markets: the AiDx assist. AiDx has chosen Nigeria for the first market introduction since Temitope has his prime partners there. The idea for creating the start-up started with developing a device that can automatically detect malaria parasites in a blood sample as an improvement for the current gold standard microscopy method. This report summarizes the exploration into the malaria market in Nigeria, intending to create a proposition for a market introduction of the device: a proposition that is both viable and desirable for stakeholders in the market.

Large scale, highly sensitive and specific diagnostic tools are needed to eliminate the transmission of schistosomiasis. This report expands upon the novel approach from M. Hoeboer and P.M. Nijman. Their Smart Optical Diagnostic Of Schistosomiasis device (SODOS), combining the principles of flow cytometry and digital holography, was able to detect schistosomiasis haematobium eggs in a 10 ml urine sample. However the mechanical complexity made it difficult to control the flow and more than 650 frames needed to be processed for each sample. The reason for this was the small flow cell volume examined within each frame. This report shows it is possible to increase the volume while maintaining the required resolution for accurate diagnosis. The size of the volume is limited by the sensor’s pixel pitch, the sensor area, the shutter time of the sensor, the thickness of the volume, the density of particles and to a lesser extent the distance of the volume to the sensor and the wavelength of the source. The density of particles in combination with the thickness of the volume proved to be
the main limiting factors. With to many particles the scattered light causes a cloudy, speckle pattern on the sensor, which does not give the desired resolution upon reconstruction. By using a 24MP APS­C CMOS colour sensor, present in the Canon EOS M50 camera, in combination with a larger volume it was possible to reconstruct eggs throughout 1.5 ml of urine using a single frame, decreasing the total required number of frames to 7. The volume thickness was 5 mm. Increasing the thickness to analyse the required 10 ml of urine in a single frame was not feasible due to scattering. However when
immersing the eggs in a Phosphate­buffered saline solution a 14 ml volume, with a thickness of 43 mm, could be analysed successfully, showing the possibilities of implementing the technique in other fields as well. The Fraunhofer particle field holography theory proved to be a valid model when making predictions regarding the resolution throughout the volume as well as providing information on the axial position of the particles.

Reliable malaria diagnosis techniques that are suitable for point-of-care testing in high burden areas, are vital for effective treatment and monitoring of the disease. Identification of malaria parasites in Giemsa stained blood slides is currently the most widely accepted technique, but its availability is limited by the need for highly trained experts to interpret the data.
In this work, a two stage automated image classification strategy is proposed, to eliminate this dependency on human expertise. Blood slides that were photographed at 20 X magnification were used in our experiments, allowing for a larger Field of View than regular thin film microscopy at 100 X.
Erythrocytes are first localised and segmented by a Convolutional Neural Network, the architecture of which is based on U-Net, with some adaptations and improvements made for our purposes. The sensitivity and positive predictive value of the localisation were both 0.998, resulting in accurate cell counts.
A transfer learning strategy, in which the existing VGG-16 network is used as a feature extractor and combined with a new fully connected layer to predict correct activations for our classification, is then used to classify the segmented erythrocytes as either infected with Plasmodium Falciparum parasite or healthy. Sensitivity and specificity of the predicted classification were 0.795 and 0.915 respectively.
It is concluded that, although this method may not fully eliminate the need for trained experts, the algorithms proposed can be of great assistance in aiding the diagnostic decision making process.

Proper diagnostics are essential in the combat against severe diseases which mainly have big impacts in remote areas in poor countries. A focus direction within the NC4I group at DCSC, Delft University of Technology, is the development of new imaging modalities and the design and implementation of smarter algorithms for improved detection of parasitic diseases. The first part of my research exploits hyperspectral imagery (HI) as new potential imaging modality of thin blood smears that could highly improve on preparation time, labor intensiveness and use of materials. HI retrieves both spatial and spectral information of the observed objects simultaneously, thus providing the ability to discriminate near similar constituents within the blood smear. In doing so, it enables the possibility of label-free detection. In this thesis, the development and building of such a system is addressed and carried out. In the context of malaria, it is shown that HI is promising and lays a profound foundation for further exploration. The design and evaluation of improved generalizing neural networks characterize the essence of the second and larger part of the research. Several group-equivariant networks are evaluated and compared with conventional convolutional networks which shows that efficient and redefined integration of weights can help build smarter and more robust classifiers for the detection of parasites. In group-equivariant networks, re-interpreting the way feature maps are connected to one another manifests in the development of convolutional stages that equivary under an increased amount of transformations besides merely translations. It is shown that enlarging the heuristic of that transformation group (the extra amount of transformations the operations are equivariant under) significantly contributes to better performance without necessarily increasing the size or changing the architecture of the networks. Compared to the aforementioned baseline (conventional convolutional stages), the best network (being equivariant under 16 equidistant rotations and mirror reflections) improves approximately 2-fold on all relevant performance metrics, among which are accuracy, sensitivity, specificity, precision, and the F1-score which are common measures in the classification of malaria. The networks were tested on the Rajaraman database. Furthermore, the pre-trained models are used as classifiers for a different database extracted from the microscope build by AiDx medical. At least for this specific database, it is shown that the more realistic transformations the pre-trained networks equivary under, the more robust they are.

This thesis discusses developments towards a low-cost Fourier Ptychographic microscope for label free imaging of malaria parasites.
A review of the morphology and life cycle of malaria and the main diagnostic methods for its detection is followed by an introduction to Fourier Ptychography with emphasis on the underlying imaging principles and phase retrieval algorithms which are at the core of the algorithms used.
The practical realization of the Fourier Ptychographic setup with the required resolution has proven to be very challenging due to its susceptibility to errors when operating the system at its theoretical limits.
Insights from in-depth analyses of the effects of quantization noise, intensity drop-off due to angled illumination, and partial coherence are presented. These insights rule in- or out these potential error sources and help identify potential mitigations in the design.
In the final chapters the realization of the setup is described, and the results with real blood smear samples are used to illustrate the interference of the error sources. The thesis concludes with considerations for further research and recommendations for international collaboration.