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With the recent advancement in the medical diagnostic tools, multi-modality medical images are extensively utilized as a lifesaving tool. An efficient fusion of medical images can improve the performance of various medical diagnostic tools. But, gathering of all modalities for a given patient is defined as an ill-posed problem as medical images suffer from poor visibility and frequent patient dropout. Therefore, in this paper, an efficient multi-modality image fusion model is proposed to fuse multi-modality medical images. To tune the hyper-parameters of the proposed model, a multi-objective differential evolution is used. The fusion factor and edge strength metrics are utilized to form a multi-objective fitness function. Performance of the proposed model is compared with nine competitive models over fifteen benchmark images. Performance analyses reveal that the proposed model outperforms the competitive fusion models. [ABSTRACT FROM AUTHOR]

Hurricanes are tropical storms that cause immense damage to human life and property. Rapid assessment of damage caused by hurricanes is extremely important for the first responders. But this process is usually slow, expensive, labor intensive and prone to errors. The advancements in remote sensing and computer vision help in observing Earth at a different scale. In this paper, a new Convolutional Neural Network model has been designed with the help of satellite images captured from the areas affected by hurricanes. The model will be able to assess the damage by detecting damaged and undamaged buildings based upon which the relief aid can be provided to the affected people on an immediate basis. The model is composed of five convolutional layers, five pooling layers, one flattening layer, one dropout layer and two dense layers. Hurricane Harvey dataset consisting of 23,000 images of size 128 × 128 pixels has been used in this paper. The proposed model is simulated on 5750 test images at a learning rate of 0.00001 and 30 epochs with the Adam optimizer obtaining an accuracy of 0.95 and precision of 0.97. The proposed model will help the emergency responders to determine whether there has been damage or not due to the hurricane and also help those to provide relief aid to the affected people. [ABSTRACT FROM AUTHOR]

Alzheimer's disease (AD) is the most generally known neurodegenerative disorder, leading to a steady deterioration in cognitive ability. Deep learning models have shown outstanding performance in the diagnosis of AD, and these models do not need any handcrafted feature extraction over conventional machine learning algorithms. Since the 2012 AlexNet accomplishment, the convolutional neural network (CNN) has been progressively utilized by the medical community to assist practitioners to early diagnose AD. This paper explores the current cutting edge applications of CNN on single and multimodality (combination of two or more modalities) neuroimaging data for the classification of AD. An exhaustive systematic search is conducted on four notable databases: Google Scholar, IEEE Xplore, ACM Digital Library, and PubMed in June 2021. The objective of this study is to examine the effectiveness of classification approaches on AD to analyze different kinds of datasets, neuroimaging modalities, preprocessing techniques, and data handling methods. However, CNN has achieved great success in the classification of AD; still, there are a lot of challenges particularly due to scarcity of medical imaging data and its possible scope in this field. [ABSTRACT FROM AUTHOR]

Alcoholism is attributed to regular or excessive drinking of alcohol and leads to the disturbance of the neuronal system in the human brain. This results in certain malfunctioning of neurons that can be detected by an electroencephalogram (EEG) using several electrodes on a human skull at appropriate positions. It is of great interest to be able to classify an EEG activity as that of a normal person or an alcoholic person using data from the minimum possible electrodes (or channels). Due to the complex nature of EEG signals, accurate classification of alcoholism using only a small dataset is a challenging task. Artificial neural networks, specifically convolutional neural networks (CNNs), provide efficient and accurate results in various pattern-based classification problems. In this work, we apply CNN on raw EEG data and demonstrate how we achieved 98% average accuracy by optimizing a baseline CNN model and outperforming its results in a range of performance evaluation metrics on the University of California at Irvine Machine Learning (UCI-ML) EEG dataset. This article explains the stepwise improvement of the baseline model using the dropout, batch normalization, and kernel regularization techniques and provides a comparison of the two models that can be beneficial for aspiring practitioners who aim to develop similar classification models in CNN. A performance comparison is also provided with other approaches using the same dataset. [ABSTRACT FROM AUTHOR]

The images captured by the cameras contain distortions, misclassified pixels, uncertainties and poor contrast. Therefore, the multi-focus image fusion (MFIF) integrates various input image features to produce a single fused image using all its objects in focus. However, it is computationally complex, which leads to inconsistency. Hence, the MFIF method is employed to generate the fused image by integrating the fuzzy sets (FS) and convolutional neural network (CNN) to detect focused and unfocused parts in both source images. It is also compared with other competing six MFIF methods like Neutrosophic set based stationary wavelet transform (NSWT), guided filters, CNN, ensemble CNN, image fusion-based CNN and deep regression pair learning (DRPL). Benchmark datasets validate the superiority of the proposed FCNN method in terms of four non-reference assessment measures having mutual information (1.1678), edge information (0.7281), structural similarity (0.9850) and human perception (0.8020) and two reference metrics such as Peak signal-to-noise ratio (57.23) and root mean square error (1.814). [ABSTRACT FROM AUTHOR]