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.

Alzheimers Disease Diagnosis Using Enhanced Inception Network Based on Brain Magnetic Resonance Image An estimated 24 million people worldwide have dementia, the majority of whom are thought to have Alzheimeru0027s disease(AD). Nowadays, Alzheimeru0027s disease represents a significant public health concern and has been identified as a research priority. Most unfortunately, there is little chance of a cure for Alzheimeru0027s disease, and the disease is difficult to detect before the dominant characteristics such as memory loss are manifested. Therefore, the diagnosis of Alzheimeru0027s disease has become an urgent problem today. Studies have shown that mild cognitive impairment(MCI) is a state between Alzheimeru0027s disease and normal, and the chance of it turning into Alzheimeru0027s disease is high. Therefore, if machines can automatically learn the characteristics of three kinds of human brain magnetic resonance(MR) images through deep learning, and help doctors to diagnose patients with mild cognitive impairment or Alzheimeru0027s disease accurately, it will be beneficial for the early diagnosis of Alzheimeru0027s disease. In this paper, we improve the Inception(V3) neural network and further test the effectiveness of the enhanced network based on the international Alzheimeru0027s disease data set, which consists of brain magnetic resonance images. The results show that the average accuracy of our approach can reach 85.7%.