BACKGROUND AND OBJECTIVES: Microscopic images are an important part for haematologists in diagnosing various diseases in the blood cell. Changes in blood cells are caused by malaria disease, and early diagnosis can prevent the disease from entering its severe stage. METHODS: In this paper, an automated non-invasive and efficient deep learning-based framework is developed for multi-class plasmodium vivax life cycle classification and malaria diagnosis. A multi-class microscopic blood cell of different plasmodium vivax life cycle stage dataset is analysed, and a diagnostic framework is designed. Several stages of the disease are examined and augmented through various techniques to make the framework robust in real-time. Generative adversarial network is specially designed to generate extended training samples of various life cycle stages to increase robustness of the resulting model. A special transformer-based neural network vision transformer is designed to improve generalisation capabilities. Microscopic images are classified into multi classes of plasmodium vivax life cycle stage, where the keystone transformer layers extract relevant disease features from microscopic colour images, and the extracted relevant features are used to make predictive diagnostic decisions. RESULTS: The capabilities of the vision transformer are computed and analysed by statistical parameters, and the performance of the vision transformer model is compared with baseline architectures, where it was evident that the performance of the vision transformer was significantly better, reaching 90.03% accuracy. CONCLUSIONS: A comprehensive comparison of the proposed framework to the state-of-the-art methods proves its efficiency in the classification of plasmodium vivax life cycle for malaria disease identification through thin blood smear microscopic images.
- MeSH
- Histological Techniques MeSH
- Malaria * MeSH
- Plasmodium vivax * MeSH
- Life Cycle Stages MeSH
- Animals MeSH
- Check Tag
- Animals MeSH
- Publication type
- Journal Article MeSH
This paper presents a neural network simulator based on anonymized patient motions that measures, categorizes, and infers human gestures based on a library of anonymized patient motions. There is a need for a sufficient training set for deep learning applications (DL). Our proposal is to extend a database that includes a limited number of videos of human physiotherapy activities with synthetic data. As a result of our posture generator, we are able to generate skeletal vectors that depict human movement. A human skeletal model is generated by using OpenPose (OP) from multiple-person videos and photographs. In every video frame, OP represents each human skeletal position as a vector in Euclidean space. The GAN is used to generate new samples and control the parameters of the motion. The joints in our skeletal model have been restructured to emphasize their linkages using depth-first search (DFS), a method for searching tree structures. Additionally, this work explores solutions to common problems associated with the acquisition of human gesture data, such as synchronizing activities and linking them to time and space. A new simulator is proposed that generates a sequence of virtual coordinated human movements based upon a script.
- MeSH
- Databases, Factual MeSH
- Humans MeSH
- Neural Networks, Computer * MeSH
- Movement * MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
This paper deals with the vulnerability of machine learning models to adversarial examples and its implication for robustness and generalization properties. We propose an evolutionary algorithm that can generate adversarial examples for any machine learning model in the black-box attack scenario. This way, we can find adversarial examples without access to model's parameters, only by querying the model at hand. We have tested a range of machine learning models including deep and shallow neural networks. Our experiments have shown that the vulnerability to adversarial examples is not only the problem of deep networks, but it spreads through various machine learning architectures. Rather, it depends on the type of computational units. Local units, such as Gaussian kernels, are less vulnerable to adversarial examples.
- MeSH
- Algorithms MeSH
- Humans MeSH
- Neural Networks, Computer * MeSH
- Supervised Machine Learning * trends MeSH
- Pattern Recognition, Automated methods trends MeSH
- Machine Learning trends MeSH
- Check Tag
- Humans MeSH
- Publication type
- Journal Article MeSH
... receptor for antigen (BCR), 61 The T-cell surface receptor for antigen (TCR), 63 vi -- CONTENTS -- The generation ... ... also play a role, 211 -- Activation-induced cell death, 213 -- T-cell regulation, 213 -- Idioty pe networks ... ... , 218 -- The influence of genetic factors, 220 -- Regulatory immunoneuroendocrine networks, 223 -- Effects ... ... differentiation, 252 -- Cellular recognition molecules exploit the immunoglobulin gene superfamily, 252 -- 12 Adversarial ...
11th ed. xvi, 474 s. : il., tabs. ; 28 cm
... tissue distribution of MHC molecules, 56 -- MHC functions, 56 -- The T-cell receptor, 56 -- The generation ... ... for antibody, 58 A similar pattern of genes codes for the T-cell receptor, 59 The mechanisms which generate ... ... revolution, 136 Cell-mediated immunity has two arms, 140 Lymphokines are part of a complex cytokine network ... ... , 159 The influence of genetic factors, 164 Are there regulatory immuno-neuroendocrine networks? ... ... of graft rejection, 282 Matching tissue types on graft donor and recipient, 282 Agents producing general ...
Seventh edition xii, 356 stran : ilustrace, tabulky ; 28 cm
- MeSH
- Allergy and Immunology MeSH
- Autoimmune Diseases MeSH
- Immunity MeSH
- Immune System Diseases MeSH
- Transplantation Immunology MeSH
- Publication type
- Monograph MeSH
- Handbook MeSH
- Conspectus
- Patologie. Klinická medicína
- NML Fields
- alergologie a imunologie
