mRMR‐based hybrid convolutional neural network model for classification of Alzheimer's disease on brain magnetic resonance images

Abstract

Alzheimer's disease is a progressive neurodegenerative fatal disease characterized by a decrease in mental functions. Although there is no definitive treatment for the disease, there are some treatment methods that delay the course of the disease in case of early diagnosis. Therefore, early diagnosis and classification of the disease are important to determine the most appropriate treatment. The most commonly used method for imaging the brain with a high soft‐tissue resolution is magnetic resonance imaging (MRI). Brain MRI help in the diagnosis of Alzheimer's disease with some specific imaging findings. In this study, we aimed to classify Alzheimer's disease in brain MRI using machine learning architectures. An mRMR‐based hybrid CNN was proposed in the study. First, features of MRI in Darknet53, InceptionV3, and Resnet101 models were extracted. These extracted features were concatenated. Then the obtained features were optimized using the mRMR method. SVM and KNN classifiers were used to classify the optimized features. The accuracy value obtained in the proposed model was 99.1%.

Machine Learning for Diagnosis of Alzheimer’s Disease and Early Stages

“…In the last five years, more stages of the disease, including mild cognitive impairment (MCI), have been included in the classification and neural networks have gradually assumed greater importance. These classifiers normally outperformed previous accuracies, as stated in [5] (97.50%), [6] (97.51%), or [7] (99.1%). By combining them with other techniques such as latent transition analysis [8] it is possible to predict status changes in Alzheimer's disease.…”

“…The large number of samples (6,028) used it also relevant when compared with similar investigations [10]. The results show that our model has outperformed other modern experiments [5][6][7]14,24,25]. A more detailed comparison with some of the most promising investigations conducted to date is made in Table 8.…”

“…In order to generate an input small and meaningful enough to train and work with our models it is necessary to apply feature selection. For this objective, we will perform two different experiments using two methods: mRMR (as used in [7,10,19]) and the traditional PCA.…”

“…PCA is a widely used algorithm for feature selection. Given the interest and good results [7,10,19] provided by mRMR, this research finds it appropriate to compare how both techniques affect the outcome of the classification.…”

Derin Öğrenme Modellerinde Komşuluk Bileşen Analizi Yöntemi Kullanarak Çiçek Görüntülerinin Sınıflandırılması

“…Ayrıca aktivasyon katmanı olarak Relu katmanını kullanmaktadır. Normalleştirme için toplu normalleştirme ve ortaklama için maksimum ortaklama ve ortalama ortaklamayı birlikte kullanmaktadır [21].…”