A deep stacked random vector functional link network autoencoder for diagnosis of brain abnormalities and breast cancer Abstract   Automated diagnosis of twoclass brain abnormalities through magnetic resonance imaging (MRI) has progressed significantly in past few years. In contrast, there exists a limited amount of methods proposed to date for multiclass brain abnormalities detection. Such detection has shown its importance in biomedical research and has remained a challenging task. Almost all existing methods are designed using conventional machine learning approaches, however, deep learning methods, due to their advantages over machine learning, have recently achieved great success in various computer vision and medical imaging applications. In this paper, a deep neural network termed as stacked random vector functional link (RVFL) based autoencoder (SRVFLAE) is proposed to detect the multiclass brain abnormalities. The RVFL autoencoders are the basic building blocks of the proposed SRVFLAE. The main purpose of choosing RVFL as the core component of the proposed SRVFLAE is to improve the generalization capability and learning speed compared to traditional autoencoder based deep learning methods. Further, the rectified linear unit (ReLU) activation function is incorporated in the proposed deep network to provide fast and better hidden representation of input features. To evaluate the effectiveness of suggested method, two benchmark multiclass MR brain datasets such as MD1 and MD2 are considered. The scheme achieved a greater accuracy of 96.67% and 95.00% on MD1 and MD2 datasets respectively. The efficacy of the model is also tested over a standard breast cancer dataset. The results demonstrated that our deep network obtains better performance with least training time and compact network architecture compared to its counterparts.

Multi instance learning via deep CNN for multiclass recognition of Alzheimers disease In recent years, number of classification techniques for Alzheimeru0027s disease (AD) have been developed that produced methods based on the use of handcrafted machine learning and obscure deep learning models. This study proposed a new classification framework based on the combination of Unetlike 2D convolutional neural networks (CNN) and multinomial logistic regression classifier, which learns the intraslice for multiclass classification after the selection of the 3D positron emission tomography (PET) image into a sequence of 2D slices. The CNNs are performed to generate the attention features of the brain while the logistic regression incorporated to learn those specifically localized features of various classes for AD classification. At the end of the network, we used a average pooling layer before the softmax for fourclass classification problem. It can efficiently generate a flexible class of transformations and that can be trained endtoend by back propagation. The results indicated that the proposed multiinstance learning (MIL) learns region of interest (ROI) itself and thus that could help to efficiently identify the precise patterns for AD. The proposed combined Unetlike CNN with multinomial regression classifier approach achieved highest accuracy of 97.9% and 96.7% on the classification of AD and MCI, respectively. It is much higher than the performances of the conventional methods in the literature.