Application of novel DIRF feature selection algorithm for automated brain disease detection
Abstract and Figures
The brain is a complex organ and Magnetic Resonance Imaging (MRI) is the most widely used imaging modality for diagnosing brain diseases due to its superior soft tissue contrast. In clinical practice, interpreting MRI scans is laborious, time-consuming, and error-prone. While most studies focus on detecting brain tumors and multiple sclerosis, there are limited studies on atrophy, ischemia, and white matter intensity (WMI) diseases. Moreover, most studies are complex, time-consuming, computationally intensive, and specific to the detection of a particular brain disease. Our study presents a new deep learning-based ensemble model and also introduces the novel double iterative ReliefF (DIRF) feature selection algorithm to enhance the performance of automatic detection of atrophy, ischemia, and WMI using MRI images. Two pre-trained deep models, namely AlexNet and GoogleNet, and three well-known local texture descriptors, namely pyramid histogram of oriented gradients (PHOG), binary Gabor pattern (BGP), and binarized statistical image features (BSIF), were employed as feature extractors, and the extracted features were merged. The merged features were fed into the proposed double
iterative ReliefF (DIRF) feature selection algorithm, and the most important 688 features were selected. Finally, the selected features were classified by support vector machine (SVM). Our proposed DIRF-based ensemble model achieved 98.87 % accuracy using a 10-fold cross-validation strategy. Our proposed model developed using a public brain diseases MRI dataset achieved an accuracy of 95.96 %. This study demonstrates that our proposed model, with DIRF feature selection algorithm can significantly improve the diagnosis of various brain diseases, enhancing the patient care and outcomes.
![Sample CNN architecture. AlexNet consists of five convolutional and three fully connected layers. In this model, the ReLU function and dropout technique were used to minimize the vanishing gradient and overfitting problems, respectively. In the VGG model [50], AlexNet's performance was improved by replacing the large convolution filters with multiple 3 x 3 sized filters one after another. The GoogleNet[51] model with 22 layers, developed in the same year as VGG, was the winner of the ImageNet competition with an accuracy of 93.3%. In this model, a new concept called inception, which includes multi-scale convolutional transformations, is introduced. To prevent the vanishing gradient problem in deepening networks, the ResNet [52] model developed by He et al. in 2015 became the winner of the ImageNet competition with a 3.57% error rate. In this model, to solve the vanishing gradient problem, shortcuts called residual links, which take the output of one layer and add it to another layer, have been added. In DenseNet [53], another CNN model, each layer is forward-connected to the other layers. Thus, each layer can use the features of the previous layers as input. The vanishing gradient problem that occurs as the network gets deeper is reduced. MobileNet [54] is a lightweight CNN model designed for mobile applications. This model uses depthwise separable convolution blocks instead of standard convolution blocks to reduce the computational cost. In addition, in the MobileNetV2 [55] version, a pointwise convolution layer called the bottleneck has been added before the depthwise separable convolution, and its performance has been further improved. Finally, another architecture, SqueezeNet [56], was presented by Iandola et al. in 2016. This model achieved AlexNet-level accuracy with 50x fewer parameters using distributed layers.](https://www.researchgate.net/profile/Sueleyman-Yaman-3/publication/370654930/figure/fig1/AS:11431281157268087@1683766146372/Sample-CNN-architecture-AlexNet-consists-of-five-convolutional-and-three-fully-connected_Q320.jpg)
Figures - uploaded by Süleyman Yaman
Author content
All figure content in this area was uploaded by Süleyman Yaman
Content may be subject to copyright.
... These features were then evaluated using SVM, RSeslibKnn, ANN, and Ran-domForest, which are machine learning methods. In this study, three distinct dimensionality reduction techniques, namely ReliefF, principal component analysis (PCA), and double iterative ReliefF (DIRF) algorithms, were employed to reduce the dimensionality of the morphological features extracted using statistical and transfer learning methods [50]. All experiments were conducted using a tenfold crossvalidation approach. ...
Wheat is unquestionably the primary source of sustenance in human dietary intake. The cultivation areas and production capacity of wheat worldwide have been observed to increase in parallel with the growth of the global population. Wheat grains from different varieties, when mixed with durum wheat, result in a reduction in the protein content. Various types of wheat grains also exhibit the same characteristic. In this particular scenario, the significance of accurately categorizing wheat becomes more pronounced. In recent years, there has been a proliferation of studies aimed at categorizing agricultural products through the application of deep learning and machine learning methodologies. In the present study, a novel approach was introduced to simultaneously analyze deep features and image patches. This was achieved by utilizing a dataset consisting of a comprehensive collection of 8354 images, encompassing various bread wheat varieties. The classification of wheat types was carried out by employing feature extraction using three distinct methods. MobileNetV2, EfficientNetV2B0, GLCM, and Color-Space algorithms were employed to extract features from the images. Lastly, the Support Vector Machine (SVM), Random Subspace ensemble with k-Nearest Neighbors (RSeslibKnn), Artificial Neural Network (ANN), and Random Forest algorithms were employed to develop models for the classification of bread wheat images. The evaluation of the experimental performances was conducted based on the criteria of accuracy, precision, recall, F-score, and mean absolute error (MAE). In general, the obtained accuracies ranged from 91.50 to 98.65%, which demonstrates the models’ proficiency in accurately classifying the samples. When examining different algorithms, Support Vector Machines (SVM) consistently demonstrate robust performance by achieving high levels of accuracy, precision, recall, and F-scores across various feature combinations.
Skin cancer is one of the most common types of cancer in the world. If skin cancer is not treated early, it also affects the diseased area under the skin
and this threatens the treatment of the disease. In recent years, many diseases have been rapidly detected with high accuracy with artificial intelligence
methods, and the treatment process has accelerated. Convolutional neural networks, one of the artificial intelligence methods, provide very detailed
information about images, and extremely successful results are obtained in classifying images. In this study, first the data set was trained with the
EfficientNetB0 model, which is one of the convolutional neural networks models. Then, with the fully connected layer of this model, deep features of
the images were obtained. These deep features were obtained by selecting Particle Swarm Optimization and Genetic Algorithm optimization, and dif-
ferent feature combinations were created. Each of these selected feature sets was classified by the support vector machines method, and the best per-
formance results were tried to be obtained. As a result, the success of the proposed model has been proven by obtaining an accuracy rate of 89.17%.
Multiple sclerosis (MS) is a chronic demyelinating condition characterized by plaques in the white matter of the central nervous system that can be detected using magnetic resonance imaging (MRI). Many deep learning models for automated MS detection based on MRI have been presented in the literature. We developed a computationally lightweight machine learning model for MS diagnosis using a novel handcrafted feature engineering approach. The study dataset comprised axial and sagittal brain MRI images that were prospectively acquired from 72 MS and 59 healthy subjects who attended the Ozal University Medical Faculty in 2021. The dataset was divided into three study subsets: axial images only (n = 1652), sagittal images only (n = 1775), and combined axial and sagittal images (n = 3427) of both MS and healthy classes. All images were resized to 224 × 224. Subsequently, the features were generated with a fixed-size patch-based (exemplar) feature extraction model based on local phase quantization (LPQ) with three-parameter settings. The resulting exemplar multiple parameters LPQ (ExMPLPQ) features were concatenated to form a large final feature vector. The top discriminative features were selected using iterative neighborhood component analysis (INCA). Finally, a k-nearest neighbor (kNN) algorithm, Fine kNN, was deployed to perform binary classification of the brain images into MS vs. healthy classes. The ExMPLPQ-based model attained 98.37%, 97.75%, and 98.22% binary classification accuracy rates for axial, sagittal, and hybrid datasets, respectively, using Fine kNN with 10-fold cross-validation. Furthermore, our model outperformed 19 established pre-trained deep learning models that were trained and tested with the same data. Unlike deep models, the ExMPLPQ-based model is computationally lightweight yet highly accurate. It has the potential to be implemented as an automated diagnostic tool to screen brain MRIs for white matter lesions in suspected MS patients.
Many machine learning-based studies have been carried out in the literature for the detection of brain tumors using MRI data and most of what has been done in the last 6 years is based on deep learning. Most of them have been designed to work with 2D data. Since many tumor-free slice images are in the models designed in 3D, the classification performance is less than in the 2D models. However, 2D models are unsuitable for practical applications as they use the slice image representing the best tumor image. Therefore, in this study, for brain tumor classification, a new 3 (Attention-Convolutional-LSTM) 3ACL deep learning model that will work with MRI data is presented. Attention, convolutional, and LSTM structures were designed in the same learning architecture in the 3 ACL models, which had an end-to-end learning strategy. Thus, the representation power of the features was increased. In addition, since the proposed model was designed in 3 dimensions, 3D MR images were used directly in the 3ACL model without transforming the 3D MR images into 2D data. Highly representative deep features are extracted from the fully connected layer of the 3ACL model. The feature set is passed to the SVM. Besides, the weighted majority vote technique, which used SVM prediction results conveyed from all slices, improved classification achievement. BRATS 2015 and 2018 datasets were used in this study. For the BRATS 2015 and 2018 datasets, the proposed approach gave 98.90% and 99.29% accuracies, respectively.
Computer-Aided Diagnosis systems have been developed to help medical professional in their de- cision making routines towards a more accurate diagnosis. These systems process medical exams such as Magnetic Resonance (MRI) in order to quantify meaningful features. These can be used with similarity-measuring techniques in a Content-Based Image Retrieval context, or inputted into a machine learning classifier in order to support early disease detection. For cardiac MRIs, single slice descriptors have been proposed in the two-dimensional domain, shape descriptors have been proposed in the three-dimensional domain, and previous reviews have mapped these two descriptor categories. Nonetheless, no systematic review on these descriptors have looked at full cardiac MRI images sets. We have reviewed the literature by searching for descriptors that consider the whole slice set (multi- slice) or frames (multi-frame) in cardiac MRI exams. We discuss descriptors and techniques, the datasets that were used, and the different evaluation metrics. Finally, we highlight literature gaps and research opportunities.
Schizophrenia (SZ) is a mental disorder that typically emerges in late adolescence or early adulthood. It reduces the life expectancy of patients by 15 years. Abnormal behavior, perception of emotions, social relationships, and reality perception are among its most significant symptoms. Past studies have revealed that SZ affects the temporal and anterior lobes of hippocampus regions of the brain. Also, increased volume of cerebrospinal fluid (CSF) and decreased volume of white and gray matter can be observed due to this disease. Magnetic resonance imaging (MRI) is the popular neuroimaging technique used to explore structural/functional brain abnormalities in SZ disorder, owing to its high spatial resolution. Various artificial intelligence (AI) techniques have been employed with advanced image/signal processing methods to accurately diagnose SZ. This paper presents a comprehensive overview of studies conducted on the automated diagnosis of SZ using MRI modalities. First, an AI-based computer aided-diagnosis system (CADS) for SZ diagnosis and its relevant sections are presented. Then, this section introduces the most important conventional machine learning (ML) and deep learning (DL) techniques in the diagnosis of diagnosing SZ. A comprehensive comparison is also made between ML and DL studies in the discussion section. In the following, the most important challenges in diagnosing SZ are addressed. Future works in diagnosing SZ using AI techniques and MRI modalities are recommended in another section. Results, conclusion, and research findings are also presented at the end.
Brain tumors are one of the most often diagnosed malignant tumors in persons of all ages. Recognizing its grade is challenging for radiologists in health monitoring and automated determination; however, IoT can help. It is critical to detect and classify contaminated tumor locations using Magnetic Resonance Imaging (MRI) images. Numerous tumors exist, including glioma tumor, meningioma tumor, pituitary tumor, and no tumor (benign). Detecting the type of tumor and preventing it is one of the most challenging aspects of brain tumor categorization. Numerous deep learning-based approaches for categorizing brain tumors have been published in the literature. A CNN (Convolutional Neural Network), the most advanced method in deep learning, was used to detect a tumor using brain MRI images. However, there are still issues with the training procedure, which is lengthy. The main goal of this project is to develop an IoT computational system based on deep learning for detecting brain tumors in MRI images. This paper suggests combining A CNN(Convolutional Neural Network) with an STM(Long Short Term Memory), LSTMs can supplement the ability of CNN to extract features. When used for image classification, the layered LSTM-CNN design outperforms standard CNN classification. Experiments are undertaken to forecast the proposed model's performance using the Kaggle data set, which contains 3264 MRI scans. The dataset is separated into two sections: 2870 photos of training sets and 394 images of testing sets. The experimental findings demonstrate that the proposed model outperforms earlier CNN and RNN models in terms of accuracy.
Generally, soft tissue information of the brain is analyzed using Magnetic Resonance Imaging (MRI). An unusual growth of cells (tumor) in or around the brain may affect its overall functionality. Among brain tumors, gliomas that appear frequently in adults pose various challenges to radiologists. If they are not recognized properly in the initial phases, patients may face severe health issues including death. So, early and precise diagnosis of gliomas is significant for increasing the survival rate of patients. Recently, several computer-aided diagnosis (CAD) approaches have been developed for the detection and classification of gliomas. However, it is a challenging issue due to the noise as well as low sensitive boundary pixels appearing in gliomas. Considering this idea, we present a new approach based on adaptive methodologies and local texture descriptors. Here, primarily, we apply a median filter to remove noise from brain MRI images. Then, we employ adaptive decomposition strategies, namely bi-dimensional empirical mode decomposition (BEMD), and modified quasi-bivariate variational mode decomposition (MQBVMD) to attain meaningful sub-images. Later, we extract hidden texture details from the sub-images using an ELDP feature descriptor and then use a support vector machine (SVM) to distinguish brain
MRI images as low-grade (LG) and high-grade (HG). Finally, we identify the infected region of gliomas with the help of adaptive K-means clustering and morphological operations. Using these sequences of operations, the proposed framework obtained 94.44% classification accuracy and 83.14% of dice similarity coefficient (DSC) value which is relatively high when compared to existing approaches.
The emerging technologies have faster growth in and have acquired a fundamental position in analyzing novel views in the anatomy of brains. The imaging modality has prevalent use in medical science for earlier detection and diagnosis. However, precise and timely diagnosis of brain tumors is a challenging task. This paper presents a novel method, namely Border Collie Firefly Algorithm-based Generative Adversarial network (BCFA-based GAN) using spark framework for effective severity level classification in brain tumor. Here, a set of slave nodes and master node is employed for performing severity classification. Here, the pre-processing is done using Laplacian filter to eradicate clatter present in image. The generated image as a result of pre-processing is segmented wherein Deep Joint model is adapted for generating segments. Thereafter, the feature extraction is performed wherein statistical features, Texton features and Karhunen-Loeve Transform-based features are extracted using slave nodes. Support vector machine (SVM) is fed with the obtained features, wherein tumor classification is done in the master node. Finally, the result is fed to BCFA-based GAN for severity level classification. The devised BCFA is used in tuning the GAN, the devised BCFA is obtained by integrating Border Collie Optimization (BCO) into Firefly Algorithm (FA). The proposed BCFA-based GAN offered the best performance and produced high values of accuracy at 97.515%, sensitivity at 97.515% as well as specificity at 97.515%.
One of the most dangerous diseases in the world is brain tumors. After the brain tumor destroys healthy tissues in the brain, it multiplies abnormally, causing an increase in the internal pressure in the skull. If this condition is not diagnosed early, it can lead to death. Magnetic Resonance Imaging (MRI) is a diagnostic method frequently used in soft tissues with successful results. This study presented a new deep learning-based approach, which automatically detects brain tumors using Magnetic Resonance (MR) images. Convolutional and fully connected layers of a new Residual-CNN (R-CNN) model trained from scratch were used to extract deep features from MR images. The representation power of the deep feature set was increased with the features extracted from all convolutional layers. Among the deep features extracted, the 100 features with the highest distinctiveness were selected with a new multi-level feature selection algorithm named L1NSR. The best performance in the classification stage was obtained by using the SVM algorithm with the Gaussian kernel. The proposed approach was evaluated on two separate data sets composed of 2-class (healthy and tumor) and 4-class (glioma tumor, meningioma tumor, pituitary tumor, and healthy) datasets. Besides, the proposed approach was compared with other state-of-the-art approaches using the respective datasets. The best classification accuracies for 2-class and 4-class datasets were 98.8% and 96.6%, respectively.
Kidney stone is a commonly seen ailment and is usually detected by urologists using computed tomography (CT) images. It is difficult and time-consuming to detect small stones in CT images. Hence, an automated system can help clinicians to detect kidney stones accurately. In this work, a novel transfer learning-based image classification method (ExDark19) has been proposed to detect kidney stones using CT images. The iterative neighborhood component analysis (INCA) is employed to select the most informative feature vectors and these selected features vectors are fed to the k nearest neighbor (kNN) classifier to detect kidney stones with a ten-fold cross-validation (CV) strategy. The proposed ExDark19 model yielded an accuracy of 99.22% with 10-fold CV and 99.71% using the hold-out validation method. Our results demonstrate that the proposed ExDark19 detect kidney stones over 99% accuracies for two validation techniques. This developed automated system can assist the urologists to validate their manual screening of kidney stones and hence reduce the possible human error.