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Machine learning (ML) often provides applicable high-performance models to facilitate decision-makers in various fields. However, this high performance is achieved at the expense of the interpretability of these models, which has been criticized by practitioners and has become a significant hindrance in their application. Therefore, in highly sensitive decisions, black boxes of ML models are not recommended. We proposed a novel methodology that uses complex supervised ML models and transforms them into simple, interpretable, transparent statistical models. This methodology is like stacking ensemble ML in which the best ML models are used as a base learner to compute relative feature weights. The index of these weights is further used as a single covariate in the simple logistic regression model to estimate the likelihood of an event. We tested this methodology on the primary dataset related to cardiovascular diseases (CVDs), the leading cause of mortalities in recent times. Therefore, early risk assessment is an important dimension that can potentially reduce the burden of CVDs and their related mortality through accurate but interpretable risk prediction models. We developed an artificial neural network and support vector machines based on ML models and transformed them into a simple statistical model and heart risk scores. These simplified models were found transparent, reliable, valid, interpretable, and approximate in predictions. The findings of this study suggest that complex supervised ML models can be efficiently transformed into simple statistical models that can also be validated. [ABSTRACT FROM AUTHOR]

Brain tumor detection depicts a tough job because of its shape, size and appearance variations. In this manuscript, a deep learning model is deployed to predict input slices as a tumor (unhealthy)/non-tumor (healthy). This manuscript employs a high pass filter image to prominent the inhomogeneities field effect of the MR slices and fused with the input slices. Moreover, the median filter is applied to the fused slices. The resultant slices quality is improved with smoothen and highlighted edges of the input slices. After that, based on these slices' intensity, a 4-connected seed growing algorithm is applied, where optimal threshold clusters the similar pixels from the input slices. The segmented slices are then supplied to the fine-tuned two layers proposed stacked sparse autoencoder (SSAE) model. The hyperparameters of the model are selected after extensive experiments. At the first layer, 200 hidden units and at the second layer 400 hidden units are utilized. The testing is performed on the softmax layer for the prediction of the images having tumors and no tumors. The suggested model is trained and checked on BRATS datasets i.e., 2012(challenge and synthetic), 2013, and 2013 Leaderboard, 2014, and 2015 datasets. The presented model is evaluated with a number of performance metrics which demonstrates the improved performance. [ABSTRACT FROM AUTHOR]

Accurate glioma detection using magnetic resonance imaging (MRI) is a complicated job. In this research, deep learning model is presented for glioma and stroke lesion detection. The proposed architecture consists of 14 layers. The first input layer is followed by three convolutional layers while 5th, 6th and 7th layers correspond to batch normalization, followed by next three layers of rectified linear unit (ReLU). Eleventh layer is average pooling 2D which is followed by fully connected (FC), softmax and classification layers respectively. The presented method is verified on six MICCAI databases namely multimodal brain tumor segmentation (BRATS) 2013, 2014, 2015, 2016, 2017 and sub-acute ischemic stroke lesion segmentation (SISS-ISLES) 2015. The computational time is also measured across each benchmark dataset such as 53 s on BRATS 2013, 26 s on BRATS 2014, 41 s on BRATS 2015, 36 s on BRATS 2016, and 38 s on BRATS 2017 and 4.13 s on ISLES 2015 proving that the proposed technique has less processing time. The proposed method achieved 0.9943 ACC, 1.00 SP, 0.9839 SE on BRATS 2013, 0.9538 ACC, 0.9991 SP, 0.7196 SE on BRATS 2014, 0.9978 ACC, 1.00 SP, 0.9919 SE on BRATS 2015, 0.9569 ACC, 0.9491 SP, 0.9755 SE on BRAST 2016, 0.9778 ACC, 0.9770 SP, 0.9789 SE on BRATS 2017 and 0.9227 ACC, 1.00 SP, 0.8814 SP on ISLES 2015 datasets respectively. [ABSTRACT FROM AUTHOR]

• Artificial bee colony based contrast stretching is perform. • Lesion detection through faster RCNN along with pixels information. • Deep features are extracted using entropy based activation function. • A Newton Raphson based most discriminate features are selected. Melanoma is the fatal form of skin cancer; however, its diagnosis at the primary stages significantly reduces the mortality rate. These days, the increasing numbers of skin cancer patients have boosted the requirement for a care decision support system - capable of detecting the lesions with high accuracy. In this work, a method is proposed for skin cancer localization and recognition by implementing a novel combination of a deep learning model and iteration-controlled Newton-Raphson (IcNR) based feature selection method. The proposed framework follows three primary steps - lesion localization through faster region based convolutional neural network (RCNN), deep feature extraction, and feature selection by IcNR approach. In the localization step, a new contrast stretching approach based on bee colony method (ABC) is being followed. The enhanced images along with their ground truths are later plugged into Fast-RCNN to get segmented images. A pre-trained model, DenseNet201, is utilized to extract deep features via transfer learning, which are later subjected to selection step using proposed IcNR approach. The selected most discriminant features are finally utilized for classification using multilayered feed forward neural networks. Tests are performed on ISBI2016 and ISBI2017 datasets to achieving an accuracy of 94.5% and 93.4%, respectively. Simulation results reveal that the proposed technique outperforms existing methods with greater accuracy, and time. [ABSTRACT FROM AUTHOR]

• Manual identification of different fish species is a difficult task. • We proposed a framework based on the CNN for fish species identification. • The proposed CNN architecture contains 32 deep layers. • We developed a data set termed as Fish-Pak from the tropical area of Pakistan. • The proposed CNN architecture is compared with state of the art CNN models. Morphological based fish species identification is an erroneous and time-consuming process. There are numerous fish species and due to their close resemblance with each other, it is difficult to classify them by external characters. Recently, computer vision and deep learning-based identification of different animal species is being widely used by the researchers. Convolutional Neural Network (CNN) is one of the most analytically powerful tools in deep learning architecture for the image classification based on visual features. This work aims to propose a deep learning framework based on the CNN method for fish species identification. The proposed CNN architecture contains 32 deep layers that are considerably deep to derive valuable and discriminating features from the image. The deep supervision is inflicted on the VGGNet architecture to increase the classification performance by instantly adding four convolutional layers to the training of each level in the network. To test the performance of proposed 32-Layer CNN architecture, we developed a dataset termed as Fish-Pak and is publicly available at Mendeley data (Fish-Pak: https://doi.org/10.17632/n3ydw29sbz.3#folder-6b024354-bae3-460a-a758-352685ba0e38). Fish-Pak contains 915 images with six distinct classes; Ctenopharyngodon idella (Grass carp), Cyprinus carpio (Common carp), Cirrhinus mrigala (Mori), Labeo rohita (Rohu), Hypophthalmichthys molitrix (Silver carp), and Catla catla (Thala) and three different image views (head region, body shape, and scale). To ensure the superior performance of proposed CNN architecture, we have carried out the experimental comparison with other deep learning frameworks involving VGG-16 for transfer learning, one block VGG, two block VGG, three block VGG, LeNet-5, AlexNet, GoogleNet, and ResNet-50 on the Fish-Pak data set. Comprehensive empirical analyses reveal that the proposed method achieves state of the art performance and outperforms existing methods. [ABSTRACT FROM AUTHOR]