Optimized Naive‐Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification

Tahmassebi, Amirhessam; Gandomi, Amir H.; Schulte, Mieke H J; Goudriaan, Anna E.; Foo, Simon Y.; Meyer‐Baese, Anke

doi:10.1155/2018/2740817

Cited by 23 publications

(18 citation statements)

References 59 publications

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“…Still, the Naïve Bayes classification fails in independent predictors; the zero-probability problem makes it challenging to implement in the multi-objective-based domain. The Naïve Bayes classifiers are not suitable to handle unsupervised data classification [ 44 ]. The Decision Tree [ 45 ] algorithm is a widely used approach for skin disease classification, prediction of lower limbs ulcers and cervical cancer.…”

Section: Related Workmentioning

confidence: 99%

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu

SivaSai

Ijaz

et al. 2021

Sensors

491

169

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Deep learning models are efficient in learning the features that assist in understanding complex patterns precisely. This study proposed a computerized process of classifying skin disease through deep learning based MobileNet V2 and Long Short Term Memory (LSTM). The MobileNet V2 model proved to be efficient with a better accuracy that can work on lightweight computational devices. The proposed model is efficient in maintaining stateful information for precise predictions. A grey-level co-occurrence matrix is used for assessing the progress of diseased growth. The performance has been compared against other state-of-the-art models such as Fine-Tuned Neural Networks (FTNN), Convolutional Neural Network (CNN), Very Deep Convolutional Networks for Large-Scale Image Recognition developed by Visual Geometry Group (VGG), and convolutional neural network architecture that expanded with few changes. The HAM10000 dataset is used and the proposed method has outperformed other methods with more than 85% accuracy. Its robustness in recognizing the affected region much faster with almost 2× lesser computations than the conventional MobileNet model results in minimal computational efforts. Furthermore, a mobile application is designed for instant and proper action. It helps the patient and dermatologists identify the type of disease from the affected region’s image at the initial stage of the skin disease. These findings suggest that the proposed system can help general practitioners efficiently and effectively diagnose skin conditions, thereby reducing further complications and morbidity.

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Section: Related Workmentioning

confidence: 99%

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Srinivasu

SivaSai

Ijaz

et al. 2021

Sensors

491

169

View full text Add to dashboard Cite

show abstract

“…Alamdari and Fatemizadeh (2013), Douglas, Harris, Yuille, and Cohen (2011), Richiardi, Eryilmaz, Schwartz, Vuilleumier, and Van De Ville (2010), and Tahmassebi et al (2018) This classifier efficiently searches a subset of voxels in fMRI data to maximize the gap in classes.…”

Section: Ensemblementioning

confidence: 99%

The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review

Rashid

Singh²,

Goyal

2020

Expert Systems

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Functional Magnetic Resonance Imaging (fMRI) is presently one of the most popular techniques for analysing the dynamic states in brain images using various kinds of algorithms. From the last decade, there is an exponential rise in the use of the machine and deep learning algorithms of artificial intelligence for analysing fMRI data. However, it is a big challenge for every researcher to choose a suitable machine or deep learning algorithm for analysing fMRI data due to the availability of a large number of algorithms in the literature. It takes much time for each researcher to know about the various approaches and algorithms which are in use for fMRI data. This paper provides a review in a systematic manner for the present literature of fMRI data that makes use of the machine and deep learning algorithms. The major goals of this review paper are to (a) identify machine learning and deep learning research trends for the implementation of fMRI; (b) identify usage of Machine Learning Algorithms and deep learning in fMRI, and (c) help new researchers based on fMRI to put their new findings appropriately in existing domain of fMRI research. The results of this systematic review identified various fMRI studies and classified them based on fMRI types, mental diseases, use of machine learning and deep learning algorithms. The authors have provided the studies with the best performance of machine learning and deep learning algorithms used in fMRI. The authors believe that this systematic review will help incoming researchers on fMRI in their future works.

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“…Computer science can benefit a lot from fMRI-based brain research [10]. With the development of time, more and more machine learning methods have been found to mine fMRI data, understand the deeper thinking process of the brain [11], and find the relationship between fMRI data and related tasks [12]. By processing the different states and types of data, machine learning can be used to classify the fMRI data, including independent component analysis (ICA) [13], support vector machine (SVM) [14,15], k-nearest neighbor (kNN) [16], Gauss naive Bayes (GNB), linear discriminant analysis (LDA) [17], logistic regression (LR) [18], autoencode [19], deep neural network (DNN), and convolutional neural network (CNN).…”

Section: Introductionmentioning

confidence: 99%

The Impact of Emotional Music on Active ROI in Patients with Depression Based on Deep Learning: A Task-State fMRI Study

Gui

Chen

Nie

2019

Computational Intelligence and Neuroscience

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With the continuous development of science, more and more research results have proved that machine learning is capable of diagnosing and studying the major depressive disorder (MDD) in the brain. We propose a deep learning network with multibranch and local residual feedback, for four different types of functional magnetic resonance imaging (fMRI) data produced by depressed patients and control people under the condition of listening to positive- and negative-emotions music. We use the large convolution kernel of the same size as the correlation matrix to match the features and obtain the results of feature matching of 264 regions of interest (ROIs). Firstly, four-dimensional fMRI data are used to generate the two-dimensional correlation matrix of one person’s brain based on ROIs and then processed by the threshold value which is selected according to the characteristics of complex network and small-world network. After that, the deep learning model in this paper is compared with support vector machine (SVM), logistic regression (LR), k-nearest neighbor (kNN), a common deep neural network (DNN), and a deep convolutional neural network (CNN) for classification. Finally, we further calculate the matched ROIs from the intermediate results of our deep learning model which can help related fields further explore the pathogeny of depression patients.

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Optimized Naive‐Bayes and Decision Tree Approaches for fMRI Smoking Cessation Classification

Cited by 23 publications

References 59 publications

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

Classification of Skin Disease Using Deep Learning Neural Networks with MobileNet V2 and LSTM

The use of machine learning and deep learning algorithms in functional magnetic resonance imaging—A systematic review

The Impact of Emotional Music on Active ROI in Patients with Depression Based on Deep Learning: A Task-State fMRI Study

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