This issue marks a transition and a changing of the guard for Computational and Mathematical Methods in Medicine (CMMM). It is with some nostalgia that we look back on our long and illustrious association with Taylor and Francis; however, at the same time we look to the future with optimism and hope as Hindawi takes the helm and converts CMMM to the community-based, open access model that they have so successfully championed. The Hindawi Publishing Corporation is one of the fastest growing academic publishers worldwide with over 200 academic journals in their portfolio and a commitment to the highest levels of peer review and excellence. 

Reflecting on the genesis and evolution of CMMM, it is clear that Brian Sleeman, the founding Editor-in-Chief, showed great foresight in creating a journal that brought together the disparate disciplines of mathematics and medicine and that continues to play a major role in the development of mathematical medicine. He worked passionately to develop and promote the journal through some difficult times, with the insight and courage to bring together both biomedical/clinical scientists and mathematical scientists onto a single editorial board (a practice that has become more commonplace in subsequent journals in the field). The success that the journal has enjoyed thus far is a clear testament to his hard work, dedication, and vision. 

The journal has continued to provide a unique forum for the dissemination of interdisciplinary research resulting from collaborations between clinicians/experimentalists and theoreticians. CMMM has also continued to evolve rapidly, reflecting the increased focus on systems and interdisciplinary collaborative efforts across the breadth of biomedical, clinical, and translational research areas. The past year also saw the result of much hard work, with the inclusion of the journal in PubMed/Medline and the Science Citation Index Expanded. This was a great development for the journal since it not only has had an enormous impact on the general awareness and profile of the journal but has also resulted in increased submissions and downloads from the journal website over the past year. It has been exciting and rewarding to see the journal develop and evolve in this manner, and we look forward to increased success following this higher profile.

The future looks extremely bright for the field of mathematical medicine as it emerges from its period of infancy and takes its place as a legitimate and central field of research and enquiry. Our sincere hope and wish is that CMMM continues from strength to strength and fulfills its role and promise as envisioned originally by its founding editor.


Pamela Jones

Sivabal Sivaloganathan

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Computational and Mathematical Methods in Medicine

One of the first signs of Alzheimer’s disease (AD) is mild cognitive impairment (MCI), in which there are small variants of brain changes among the intermediate stages. Although there has been an increase in research into the diagnosis of AD in its early levels of developments lately, brain changes, and their complexity for functional magnetic resonance imaging (fMRI), makes early detection of AD difficult. This paper proposes a deep learning-based method that can predict MCI, early MCI (EMCI), late MCI (LMCI), and AD. The Alzheimer’s Disease Neuroimaging Initiative (ADNI) fMRI dataset consisting of 138 subjects was used for evaluation. The finetuned ResNet18 network achieved a classification accuracy of 99.99%, 99.95%, and 99.95% on EMCI vs. AD, LMCI vs. AD, and MCI vs. EMCI classification scenarios, respectively. The proposed model performed better than other known models in terms of accuracy, sensitivity, and specificity.

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Analysis of Features of Alzheimer's Disease: Detection of Early Stage from Functional Brain Changes in Magnetic Resonance Images Using a Finetuned ResNet18 Network.

The origin of dementia can be largely attributed to Alzheimer’s disease (AD). The progressive nature of AD causes the brain cell deterioration that eventfully leads to physical dependency and mental disability which hinders a person’s normal life. A computer-aided diagnostic system is required that can aid physicians in diagnosing AD in real-time. The AD stages classification remains an important research area. To extract the deep-features, the traditional machine learning-based and deep learning-based methods often require large dataset and that leads to class imbalance and overfitting issues. To overcome this problem, the use an efficient transfer learning architecture to extract deep features which are further used for AD stage classification. In this study, an Alzheimer’s stage detection system is proposed based on deep features using a pre-trained AlexNet model, by transferring the initial layers from pre-trained AlexNet model and extract the deep features from the Convolutional Neural Network (CNN). For the classification of extracted deep-features, we have used the widely used machine learning algorithms including support vector machine (SVM), k-nearest neighbor (KNN), and Random Forest (RF). The evaluation results of the proposed scheme show that a deep feature-based model outperformed handcrafted and deep learning method with 99.21% accuracy. The proposed model also outperforms existing state-of-the-art methods.

A deep feature-based real-time system for Alzheimer disease stage detection

Brain is the controlling center of our body. With the advent of time, newer and newer brain diseases are being discovered. Thus, because of the variability of brain diseases, existing diagnosis or detection systems are becoming challenging and are still an open problem for research. Detection of brain diseases at an early stage can make a huge difference in attempting to cure them. In recent years, the use of artificial intelligence (AI) is surging through all spheres of science, and no doubt, it is revolutionizing the field of neurology. Application of AI in medical science has made brain disease prediction and detection more accurate and precise. In this study, we present a review on recent machine learning and deep learning approaches in detecting four brain diseases such as Alzheimer’s disease (AD), brain tumor, epilepsy, and Parkinson’s disease. 147 recent articles on four brain diseases are reviewed considering diverse machine learning and deep learning approaches, modalities, datasets etc. Twenty-two datasets are discussed which are used most frequently in the reviewed articles as a primary source of brain disease data. Moreover, a brief overview of different feature extraction techniques that are used in diagnosing brain diseases is provided. Finally, key findings from the reviewed articles are summarized and a number of major issues related to machine learning/deep learning-based brain disease diagnostic approaches are discussed. Through this study, we aim at finding the most accurate technique for detecting different brain diseases which can be employed for future betterment.

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Machine Learning and Deep Learning Approaches for Brain Disease Diagnosis: Principles and Recent Advances

The prevalence of Alzheimer’s disease (AD) in the growing elderly population makes accurately predicting AD progression crucial. Due to AD’s complex etiology and pathogenesis, an effective and medically practical solution is a challenging task. In this paper, we developed and evaluated two novel hybrid deep learning architectures for AD progression detection. These models are based on the fusion of multiple deep bidirectional long short-term memory (BiLSTM) models. The first architecture is an interpretable multitask regression model that predicts seven crucial cognitive scores for the patient 2.5 years after their last observations. The predicted scores are used to build an interpretable clinical decision support system based on a glass-box model. This architecture aims to explore the role of multitasking models in producing more stable, robust, and accurate results. The second architecture is a hybrid model where the deep features extracted from the BiLSTM model are used to train multiple machine learning classifiers. The two architectures were comprehensively evaluated using different time series modalities of 1371 subjects participated in the study of the Alzheimer’s disease neuroimaging initiative (ADNI). The extensive, real-world experimental results over ADNI data help establish the effectiveness and practicality of the proposed deep learning models.

Robust hybrid deep learning models for Alzheimer’s progression detection

Analysis of brain sub regions using optimization techniques and deep learning method in Alzheimer disease

Nature-inspired algorithms play a vital role in various applications, namely image processing, engineering, industrialized designs and business. Generally, these algorithms are inspired by the nature which is helpful in segmenting the brain internal regions, namely cerebrospinal fluid, grey matter HC, white matter, ventricle and so on. Segmentation of hippocampus (HC) is a very hectic process due to its anatomical structure of the brain. This work has been recommended for different optimization techniques such as lion optimization algorithm (LOA), genetic algorithm, BAT algorithm, particle swarm optimization and artificial bee colony optimization to segment HC region from the brain subregions. The comparison of these optimization methods has been evaluated, and it showed better performance in LOA due to its individualities of escaping from local optima. From the obtained results, it is witnessed that the LOA has ability to segment HC region with high accuracy of 95%. The LOA method showed the best classification accuracy compared to all other methods. Finally, the mini-mental state examination score validation has been attempted to reach the clinical targets as HC region is a major hallmarks for diagnosing AD. The overall process of the proposed work demonstrates the abnormalities in the brain natural history which provides the reliable and accurate indication to the clinician about AD progression.

Diagnosis of Alzheimer disease in MR brain images using optimization techniques

Magnetic Resonance Imaging (MRI) is an accurate method for disease detection of a whole body. It plays a main role in the clinical and surgical environment to diagnose many diseases such as tumors, dementias, bone injuries, spinal injuries, stroke etc. Alzheimer is the one type of dementia for older adults which gives brain disorder. It is a common neurodegenerative dementia. MR Images provide benefits for identifying Alzheimer. In MR images, brain asymmetry identification is a hectic process because the normal human brain is not symmetrical nature. Proposed method defines asymmetry analysis of brain hemispheres with the help of genetic algorithm. In this work, whole brain analysis is carried out for brain MR Alzheimer images. Proposed system uses Fuzzy C Means (FCM) clustering technique is used to segment gray matter and white matter. Gray Level Co-occurrence Matrix (GLCM) is extracted from the segmented white and gray matter tissues. The same pipeline is adopted for asymmetry of the brain hemispheres. The results show that, asymmetry analysis of brain hemispheres is found to be very effective for differentiation of normal and abnormal tissues compared to whole brain analysis. This work proved is that, it has a difference between white and gray matter in which the gray matter gives better result than white matter. The quantification of this brain hemispheric change leads to improved diagnosis and better assessment of the neuroprotectic effect of a therapy. Hence this study is prominent for diagnosing Alzheimer disease.

Brain Hemisphere Analysis Using Genetic Algorithm and Fuzzy Clustering in Alzheimer Disease

Purpose study: Alzheimer is a type of dementia that usually affects older adults by creating memory loss due to damaged brain cells. The damaged brain cells lead to shrinkage in the size of the brain and it is very difficult to extract the grey matter (GM) and white matter (WM). The segmentation of GM and WM is a challenging task due to its homogeneous nature between the neighborhood tissues. In this proposed system, an attempt has been made to extract GM and WM tissues using optimization-based segmentation techniques.Materials and methods: The optimization method is considered for the classification of normal and alzheimer disease (ad) through magnetic resonance images (mri) using a modified cuckoo search algorithm. Gray Level Co-Occurrence Matrix (GLCM) features are calculated from the extracted GM and WM. Principal Component Analysis (PCA) is adopted for selecting the best features from the GLCM features. Support Vector Machine (SVM) is a classifier which is used to classify the normal and abnormal images. Results: The proposed optimization algorithm provides most promising and efficient level of image segmentation compared to fuzzy c means (fcm), otsu, particle swarm optimization (pso) and cuckoo search (cs). The modified cuckoo yields high accuracy of 96%, sensitivity of 97% and specificity of 94% than other methods due to its powerful searching potential for the proper identification of gray and WM tissues.Conclusions: The results of the classification process proved the effectiveness of the proposed technique in identifying alzheimer affected patients due to its very strong optimization ability. The proposed pipeline helps to diagnose early detection of AD and better assessment of the neuroprotective effect of a therapy.

Efficient optimization based thresholding technique for analysis of alzheimer MRIs.

In today's world of transportation, road accidents are one of the most common problems. The world health organization has released a list of the top ten causes of human death, sadly, traffic accidents are in ninth place. Even in the automobile industry, many inventories make and produce safety features however, traffic accidents are inevitable. In this paper, the machine learning concept is applied to predict the severity of the accident and analyze factors like the number of accidents by year, Number of accidents by state, Accidents on the day of the week, road accidents by state day and hours, accidents ratio between rural and urban areas, Age people involved in the accidents, most dangerous time to drive, with the help of current dataset. This will be effective in improving safety measures and reducing traffic accidents.

D. Chitradevi

Papers

Analysis of brain sub regions using optimization techniques and deep learning method in Alzheimer disease

Diagnosis of Alzheimer disease in MR brain images using optimization techniques

Brain Hemisphere Analysis Using Genetic Algorithm and Fuzzy Clustering in Alzheimer Disease

Efficient optimization based thresholding technique for analysis of alzheimer MRIs.

Predictive Analytics of Road Accidents Using Machine Learning