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A research on prediction of bat-borne disease infection through segmentation using diffusion-weighted MR imaging in deep-machine learning approach
Introduction
Early prognosis of viral infections can be beneficial not only for the medical profession but also for other organisms living in the community. Toxic infections that can affect the most delicate part of the human body, even when mixed with other parts of the body, can have a devastating effect on the human body, leaving it unable to control its growth for a few days, months, or years; without causing immediate damage. Often pathogens (such as West Nile, HeV, NiV, and ZiV) spread the infection in the human brain, causing inflammation and greatly reducing its functional capacity. Medical Imaging and Signal Processing (MISP) are effective and standardized diagnostic tools that help identify brain disorders. It provides a clear Neuroimaging recommendation and can easily distinguish between infected and uninfected outcomes. We have reviewed several publications; most researchers have used MRI to classify an infection by wave transformation [25]. Compared to other biomedical signals, MRI images acquire precise information at high resolution. Machine learning-based clinical diagnosis is an emerging technology. The computerized diagnostic method is a trend that can be very helpful for doctors to make a decision quickly. Artificial intelligence techniques such as machine learning and deep learning are widely used in the medical field to find the appropriate information from the patient's health analysis, both from images and textual data. In this way, computed tomography and magnetic resonance imaging are the two efficient radiological methods, highly suggested by the pathologist to reconstruct and shorten the health sequence and thus obtain good productivity from the scanner. In the worst pandemic situation, the pharmaceutical industry needs an intelligent method of disease diagnosis, a computer-aided decision-making system, and a disease detection tool to predict infection at an early stage, to reduce infections and death. We can say that machine learning is dependent on artificial intelligence, it can be defined to imitate human intelligence and process tasks in real-time such as, based on data, knowledge extraction, pattern recognition, etc.
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Section snippets
Study materials
Computer-aided NIPAH disease prognosis articles were searched in standard databases including IEEE, Elsevier, Springer, Scopus, and medical research databases, such as MDPI, Hindawi, Frontiers, and PLOS. We have found numerous chemical and biological research papers based on machine learning. But MRI information was only available during the NiV eruption. Therefore, we have taken some traditionally published brain infection research papers from the decades (the 1990 s up to the present).
Nipah virus (A Bat-Borne Disease)
In the period between 1998 and 1999, the novel type of fatal encephalitis with prodromal symptoms was discovered by the Malaysian Health government with small white matter present in the part of the cerebral cortex substantially. During this period [26] around 265 cases were diagnosed with a similar onset of symptoms (Fig. 1), 105 deaths confirmed respectively. Nipah virus is completely asymptomatic; the key is NiV encephalitis closely related to the Japanese encephalitis, the disease (JE) will
Brain encephalitis disorder
Encephalitis is related to numerous brain-oriented diseases such as Parkinson’s, Alzheimer’s, tumor, multiple sclerosis, epilepsy, headache disorders, traumatic injuries, and even other neurological infections caused by fungal-like Cryptococcus respectively. For such kind of brain disorder disease, EEG and MR imaging examinations are followed, and effective diagnosis techniques to extract the features, images of different encephalitis are presented in Fig. 2. Based on the radiological study [31]
Machine learning
Machine learning and deep learning techniques are rapidly evolving in medical research to extract meaningful patterns, read any information from the computer system, and deliver the desired result by training the model [17]. In clinical imaging and laboratory diagnosis, machine learning provides enormous visualization tools to diagnose inflammation through image analysis as it can be very helpful in treating the patient by extracting information. Therefore, it comes down to providing proper
Deep learning in medical imaging
Deep learning plays an important role in the field of medical care through clinical picture analysis, blood analysis, lesion diagnosis, disease prognosis, and object detection. DL structures such as Tensorflow, Bydarch, and MXnet are used to learn patterns from image-type data. Therefore, ML and DL algorithms based on artificial intelligence are more effective in finding the optimal solution compared to the normal mathematical solution [33].
Result and discussion
In particular, early detection of brain injury may be an opportunity to increase the life expectancy of the affected patient in the absence of proper medication for the diagnosed disease. In clinical image analysis, therapists constantly focus on deep learning to obtain the best quality of processing results. As we all know, the Nipah virus outbreak is the most familiar and recognizable disease in Kozhikode, Kerala in 2018. There were about seventeen deaths during the eruption. Since then, the
Conclusion
Deep neural networks such as CNN, U-NET, and ResNet can be very effective in detecting abnormalities in the brain (Table 1). Determining the presence of Nipah encephalitis (NiVE)/gray-white matter is challenging for two main reasons: i) the biological structure of the cerebrospinal fluid and ii) the prodromal infection. Through this review, radiologists have purchased T1, T2, or FLAIR images to diagnose common brain diseases such as tumors. In the case of the Nipah virus, a diffusion-weighted
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
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