Optimasi Klasifikasi Parasit Malaria Dengan Metode LVQ, SVM dan Backpropagation

The use of the classification method affects the accuracy of the test results. The accuracy of the classification method is affected by the number of classes in the image. The number of classes and the amount of data should be considered when making decisions in choosing a classification method. This study used 600 data, which were divided into 510 training data and 90 test data. The number of classes tested is 12 classes with the number of initial features used by 22 features. The characteristics used in the test consist of shape characteristics and texture characteristics. The classification methods used in this study are LVQ, Backpropagation, and SVM. The data has 22 features or attributes that are the result of texture and shape feature extraction. Texture features are energy 0o, energy 45o, energy 90o, energy 135o, entropy 0o, entropy 45o, entropy 90o, entropy 135o, contrast 0o, contrast 45o, contrast 90o, contrast 135o, homogeneity 00, homogeneity 45o, homogeneity 90o, homogeneity 135o, correlation 0o, Correlation 45o, correlation 90o, correlation 135o, features of área and perimeter shape. The test results using the Backpropagation method obtained 89.7% results, using the LVQ method obtained 77.78% results, and the SVM method obtained 99.1% results

On the use of deep learning for phase recovery

Phase recovery (PR) refers to calculating the phase of the light field from its intensity measurements. As exemplified from quantitative phase imaging and coherent diffraction imaging to adaptive optics, PR is essential for reconstructing the refractive index distribution or topography of an object and correcting the aberration of an imaging system. In recent years, deep learning (DL), often implemented through deep neural networks, has provided unprecedented support for computational imaging, leading to more efficient solutions for various PR problems. In this review, we first briefly introduce conventional methods for PR. Then, we review how DL provides support for PR from the following three stages, namely, pre-processing, in-processing, and post-processing. We also review how DL is used in phase image processing. Finally, we summarize the work in DL for PR and outlook on how to better use DL to improve the reliability and efficiency in PR. Furthermore, we present a live-updating resource (https://github.com/kqwang/phase-recovery) for readers to learn more about PR.Comment: 82 pages, 32 figure

Peripheral Blood Smear Analyses Using Deep Learning

Peripheral Blood Smear (PBS) analysis is a vital routine test carried out by hematologists to assess some aspects of humans’ health status. PBS analysis is prone to human errors and utilizing computer-based analysis can greatly enhance this process in terms of accuracy and cost. Recent approaches in learning algorithms, such as deep learning, are data hungry, but due to the scarcity of labeled medical images, researchers had to find viable alternative solutions to increase the size of available datasets. Synthetic datasets provide a promising solution to data scarcity, however, the complexity of blood smears’ natural structure adds an extra layer of challenge to its synthesizing process. In this thesis, we propose a method- ology that utilizes Locality Sensitive Hashing (LSH) to create a novel balanced dataset of synthetic blood smears. This dataset, which was automatically annotated during the gener- ation phase, covers 17 essential categories of blood cells. The dataset also got the approval of 5 experienced hematologists to meet the general standards of making thin blood smears. Moreover, a platelet classifier and a WBC classifier were trained on the synthetic dataset. For classifying platelets, a hybrid approach of deep learning and image processing tech- niques is proposed. This approach improved the platelet classification accuracy and macro- average precision from 82.6% to 98.6% and 76.6% to 97.6% respectively. Moreover, for white blood cell classification, a novel scheme for training deep networks is proposed, namely, Enhanced Incremental Training, that automatically recognises and handles classes that confuse and negatively affect neural network predictions. To handle the confusable classes, we also propose a procedure called "training revert". Application of the proposed method has improved the classification accuracy and macro-average precision from 61.5% to 95% and 76.6% to 94.27% respectively. In addition, the feasibility of using animal reticulocyte cells as a viable solution to com- pensate for the deficiency of human data is investigated. The integration of animal cells is implemented by employing multiple deep classifiers that utilize transfer learning in differ- ent experimental setups in a procedure that mimics the protocol followed in experimental medical labs. Moreover, three measures are defined, namely, the pretraining boost, the dataset similarity boost, and the dataset size boost measures to compare the effectiveness of the utilized experimental setups. All the experiments of this work were conducted on a novel public human reticulocyte dataset and the best performing model achieved 98.9%, 98.9%, 98.6% average accuracy, average macro precision, and average macro F-score re- spectively. Finally, this work provides a comprehensive framework for analysing two main blood smears that are still being conducted manually in labs. To automate the analysis process, a novel method for constructing synthetic whole-slide blood smear datasets is proposed. Moreover, to conduct the blood cell classification, which includes eighteen blood cell types and abnormalities, two novel techniques are proposed, namely: enhanced incremental train- ing and animal to human cells transfer learning. The outcomes of this work were published in six reputable international conferences and journals such as the computers in biology and medicine and IEEE access journals

Mobile Diagnosis 2.0

Mobile sensing and diagnostic capabilities are becoming extremely important for a wide range of emerging applications and fields spanning mobile health, telemedicine, point-of-care diagnostics, global health, field medicine, democratization of sensing and diagnostic tools, environmental monitoring, and citizen science, among many others. The importance of low-cost mobile technologies has been underlined during this current COVID-19 pandemic, particularly for applications such as the detection of pathogens, including bacteria and viruses, as well as for prediction and management of different diseases and disorders. This book focuses on some of these application areas and provides a timely summary of cutting-edge results and emerging technologies in these interdisciplinary fields

MODELLING AND VERIFICATION OF THERMOACOUSTIC MEDICAL IMAGING FROM NANOSCOPIC TO MACROSCOPIC RESOLUTIONS

In this thesis, three main questions regarding the potential of thermoacoustic imaging are answered: 1) what are the conventional resolution limitations of photoacoustic imaging and how can they be extended to enable high-resolution imaging, 2) Can photoacoustic imaging resolution be brought down to nanoscopic levels, and 3) As laser based photoacoustic imaging has been deployed with great success, is it also possible for other radiation to generate useful ultrasound signals for imaging? Whereas laser-induced photoacoustic tomography has been widely explored for a diverse range of biomedical contexts, there remain some fundamental limits to the resolution levels in which it can operate. Namely, the axial resolution of photoacoustic imaging remains restricted by the fact that ultrasonic transducers are not able to detect high-frequency signals that encode nanoscale resolution information. Therefore, there is a lingering question about how photoacoustic imaging can truly enter the realm of nanoscale imaging, as has been done by other modalities such as STED microscopy, structured illumination microscopy, and STORM microscopy. It is believed that laser-based detection in lieu of a transducer may enable a super-resolution photoacoustic imaging modality. However, there remain important questions about the reach and feasibility of nanoscale photoacoustic imaging. Specifically: will highly focused lasers directed at single cells result in thermal damage of biological samples? Will the axial imaging resolution of laser based detection truly be able to overcome the conventional optical diffraction limit of ~200nm? Will optical detection be sensitive enough to detect photoacoustic signals? Consequently, models are developed for thermoacoustic imaging for nanoscale imaging at super-resolutions exceeding that of the optical diffraction limit (~200nm), that show the potential for thermoacoustic imaging to enable super-resolution imaging of single cells. The models confirm that such imaging is possible while simultaneously ensuring the thermal safety of cells as the laser-induced temperature rise of such imaging is only within mK, potentially allowing for high-resolution imaging in vivo. It is also confirmed that a laser of 7ps duration should generate frequencies high enough to enable super-resolutions. Models are also developed for the estimation of the sensitivity and resolution of these high-resolution imaging, and it is predicted that super-resolution photoacoustic imaging may be able to image at axial resolutions of 10nm at noise equivalent number of molecules of 292 in the case of imaging hemoglobin in red blood cells. A length-scale and time-scale generalizable simulation workflow is developed and deployed to generate simulated images of super-resolution photoacoustic imaging, showing the potential of 3D super-resolution achievable via thermoacoustic imaging. This numerical simulation workflow is generalizable to multiple length scales as well as to other sources of radiation. The model predictions regarding detectable high frequency photoacoustic signal generation is experimentally confirmed via the creation and testing of a pump-probe based preliminary photoacoustic imaging system. The system is shown to be capable of detecting a clear and repeatable signal. Acquired A-lines from this system confirm that GHz frequencies can be detected using pump-probe detection in photoacoustics, thereby opening the door for nanoscale photoacoustic imaging However, the experimental results also demonstrate that feasible and convenient nanoscale imaging will require a more stable laser than is available, as pulse to pulse intensity fluctuations in the laser greatly limit the imaging speed and necessary number of averages for a single A-line scan. The developed models show promise and use towards the development of novel thermoacoustic imaging modalities and can be deployed to assess feasibility of different configurations of thermoacoustic imaging prior to the expenditure of resources on experimental realization. In this way, the developed models have the potential to enable the development of various thermoacoustic imaging modalities via a single generalizable framework through which imaging characteristics can be predicted at multiple length and time scales

2019 IMSAloquium: Student Inquiry and Research Program and IMSA Internship Program

Welcome to IMSAloquium 2019! This is IMSA’s 32nd year of leading in educational innovation, the 31st year of the IMSA Student Inquiry and Research (SIR) Program, and the first year of the newly imagined IMSA Internship Program.https://digitalcommons.imsa.edu/archives_sir/1029/thumbnail.jp

Incorporating standardised drift-tube ion mobility to enhance non-targeted assessment of the wine metabolome (LC×IM-MS)

Liquid chromatography with drift-tube ion mobility spectrometry-mass spectrometry (LCxIM-MS) is emerging as a powerful addition to existing LC-MS workflows for addressing a diverse range of metabolomics-related questions [1,2]. Importantly, excellent precision under repeatability and reproducibility conditions of drift-tube IM separations [3] supports the development of non-targeted approaches for complex metabolome assessment such as wine characterisation [4]. In this work, fundamentals of this new analytical metabolomics approach are introduced and application to the analysis of 90 authentic red and white wine samples originating from Macedonia is presented. Following measurements, intersample alignment of metabolites using non-targeted extraction and three-dimensional alignment of molecular features (retention time, collision cross section, and high-resolution mass spectra) provides confidence for metabolite identity confirmation. Applying a fingerprinting metabolomics workflow allows statistical assessment of the influence of geographic region, variety, and age. This approach is a state-of-the-art tool to assess wine chemodiversity and is particularly beneficial for the discovery of wine biomarkers and establishing product authenticity based on development of fingerprint libraries