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In 2023, Turkiye faced a series of devastating earthquakes and these earthquakes affected millions of people due to damaged constructions. These earthquakes demonstrated the urgent need for advanced automated damage detection models to help people. This study introduces a novel solution to address this challenge through the AttentionPoolMobileNeXt model, derived from a modified MobileNetV2 architecture. To rigorously evaluate the effectiveness of the model, we meticulously curated a dataset comprising instances of construction damage classified into five distinct classes. Upon applying this dataset to the AttentionPoolMobileNeXt model, we obtained an accuracy of 97%. In this work, we have created a dataset consisting of five distinct damage classes, and achieved 97% test accuracy using our proposed AttentionPoolMobileNeXt model. Additionally, the study extends its impact by introducing the AttentionPoolMobileNeXt-based Deep Feature Engineering (DFE) model, further enhancing the classification performance and interpretability of the system. The presented DFE significantly increased the test classification accuracy from 90.17% to 97%, yielding improvement over the baseline model. AttentionPoolMobileNeXt and its DFE counterpart collectively contribute to advancing the state-of-the-art in automated damage detection, offering valuable insights for disaster response and recovery efforts. [ABSTRACT FROM AUTHOR]

This work aims to develop a novel convolutional neural network (CNN) named ResNet50* to detect various gastrointestinal diseases using a new ResNet50*-based deep feature engineering model with endoscopy images. The novelty of this work is the development of ResNet50*, a new variant of the ResNet model, featuring convolution-based residual blocks and a pooling-based attention mechanism similar to PoolFormer. Using ResNet50*, a gastrointestinal image dataset was trained, and an explainable deep feature engineering (DFE) model was developed. This DFE model comprises four primary stages: (i) feature extraction, (ii) iterative feature selection, (iii) classification using shallow classifiers, and (iv) information fusion. The DFE model is self-organizing, producing 14 different outcomes (8 classifier-specific and 6 voted) and selecting the most effective result as the final decision. During feature extraction, heatmaps are identified using gradient-weighted class activation mapping (Grad-CAM) with features derived from these regions via the final global average pooling layer of the pretrained ResNet50*. Four iterative feature selectors are employed in the feature selection stage to obtain distinct feature vectors. The classifiers k-nearest neighbors (kNN) and support vector machine (SVM) are used to produce specific outcomes. Iterative majority voting is employed in the final stage to obtain voted outcomes using the top result determined by the greedy algorithm based on classification accuracy. The presented ResNet50* was trained on an augmented version of the Kvasir dataset, and its performance was tested using Kvasir, Kvasir version 2, and wireless capsule endoscopy (WCE) curated colon disease image datasets. Our proposed ResNet50* model demonstrated a classification accuracy of more than 92% for all three datasets and a remarkable 99.13% accuracy for the WCE dataset. These findings affirm the superior classification ability of the ResNet50* model and confirm the generalizability of the developed architecture, showing consistent performance across all three distinct datasets. [ABSTRACT FROM AUTHOR]

In the field of nanoscience, the scanning electron microscope (SEM) is widely employed to visualize the surface topography and composition of materials. In this study, we present a novel SEM image classification model called NFSDense201, which incorporates several key components. Firstly, we propose a unique nested patch division approach that divides each input image into four patches of varying dimensions. Secondly, we utilize DenseNet201, a deep neural network pretrained on ImageNet1k, to extract 2920 deep features from the last fully connected and global average pooling layers. Thirdly, we introduce an iterative neighborhood component analysis function to select the most discriminative features from the merged feature vector, which is formed by concatenating the four feature vectors extracted per input image. This process results in a final feature vector of optimal length 698. Lastly, we employ a standard shallow support vector machine classifier to perform the actual classification. To evaluate the performance of NFSDense201, we conducted experiments using a large public SEM image dataset. The dataset consists of 972, 162, 326, 4590, 3820, 3925, 4755, 181, 917, and 1624.jpeg images belonging to the following microstructural categories: "biological," "fibers," "film-coated surfaces," "MEMS devices and electrodes," "nanowires," "particles," "pattern surfaces," "porous sponge," "powder," and "tips," respectively. For both four-class and ten-class classification tasks, we evaluated NFSDense201 using subsets of the dataset containing 5080 and 21,272 images, respectively. The results demonstrate the superior performance of NFSDense201, achieving a four-class classification accuracy rate of 99.53% and a ten-class classification accuracy rate of 97.09%. These accuracy rates compare favorably against previously published SEM image classification models. Additionally, we report the performance of NFSDense201 for each class in the dataset. [ABSTRACT FROM AUTHOR]

Background: Ankylosing spondylitis (AS) is a chronic, painful, progressive disease usually seen in the spine. Traditional diagnostic methods have limitations in detecting the early stages of AS. The early diagnosis of AS can improve patients' quality of life. This study aims to diagnose AS with a pre-trained hybrid model using magnetic resonance imaging (MRI). Materials and Methods: In this research, we collected a new MRI dataset comprising three cases. Furthermore, we introduced a novel deep feature engineering model. Within this model, we utilized three renowned pretrained convolutional neural networks (CNNs): DenseNet201, ResNet50, and ShuffleNet. Through these pretrained CNNs, deep features were generated using the transfer learning approach. For each pretrained network, two feature vectors were generated from an MRI. Three feature selectors were employed during the feature selection phase, amplifying the number of features from 6 to 18 (calculated as 6 × 3). The k-nearest neighbors (kNN) classifier was utilized in the classification phase to determine classification results. During the information phase, the iterative majority voting (IMV) algorithm was applied to secure voted results, and our model selected the output with the highest classification accuracy. In this manner, we have introduced a self-organized deep feature engineering model. Results: We have applied the presented model to the collected dataset. The proposed method yielded 99.80%, 99.60%, 100%, and 99.80% results for accuracy, recall, precision, and F1-score for the collected axial images dataset. The collected coronal image dataset yielded 99.45%, 99.20%, 99.70%, and 99.45% results for accuracy, recall, precision, and F1-score, respectively. As for contrast-enhanced images, accuracy of 95.62%, recall of 80.72%, precision of 94.24%, and an F1-score of 86.96% were attained. Conclusions: Based on the results, the proposed method for classifying AS disease has demonstrated successful outcomes using MRI. The model has been tested on three cases, and its consistently high classification performance across all cases underscores the model's general robustness. Furthermore, the ability to diagnose AS disease using only axial images, without the need for contrast-enhanced MRI, represents a significant advancement in both healthcare and economic terms. [ABSTRACT FROM AUTHOR]

In February 2023, Turkey experienced a series of earthquakes that caused significant damage to buildings and affected many people. Detecting building damage quickly is crucial for helping earthquake victims, and we believe machine learning models offer a promising solution. In our research, we introduce a new, lightweight deep-learning model capable of accurately classifying damaged buildings in remote-sensing datasets.Our main goal is to create an automated damage detection system using a novel deep-learning model. We started by collecting a new dataset with two categories: damaged and undamaged buildings. Then, we developed a unique convolutional neural network (CNN) called the inception and concatenation residual (InCR) deep learning network, which incorporates concatenation-based residual blocks and inception blocks to improve performance.We trained our InCR model on the newly collected dataset and used it to extract features from images using global average pooling. To refine these features and select the most informative ones, we applied iterative neighborhood component analysis (INCA). Finally, we classified the refined features using commonly used shallow classifiers.To evaluate our method, we used tenfold cross-validation (10-fold CV) with eight classifiers. The results showed that all classifiers achieved classification accuracies higher than 98 %. This demonstrates that our proposed InCR model is a viable option for CNNs and can be used to create an accurate automated damage detection application.Our research presents a unique solution to the challenge of automated damage detection after earthquakes, showing promising results that highlight the potential of our approach.

We are living in the information era. Therefore, intelligence-based researchers are hot-topic such as artificial intelligence. In the artificial intelligence research area, machine learning and deep learning models have frequently used to create intelligence assistants and deep learning is the shining star of the AI. Specifically, in the computer vision, numerous deep learning models have been proposed, leading to a competition between transformers and convolutional neural networks (CNNs). Since the introduction of Vision Transformers (ViT), many transformer models have been advocated for computer vision, often overshadowing CNNs. Therefore, it is crucial to propose CNNs to showcase their prowess in image classification. This research introduces a lightweight CNN named MobileDenseNeXt. The proposed MobileDenseNeXt comprises four main blocks: (i) input, (ii) main, (iii) average pooling-based downsampling, and (iv) output. This research also incorporates convolution-based residual blocks and uses a depth concatenation layer to increase the number of filters. For downsampling, an average pooling operation has been employed, similar to the original DenseNet. Furthermore, the swish activation function is utilized in the presented CNN. MobileDenseNeXt has approximately 1.4 million learnable parameters, categorizing it as a lightweight CNN model. Additionally, a deep feature engineering approach has been developed using MobileDenseNeXt, incorporating two feature extractors with global average pooling and dropout layers, along with 10 feature selectors, to demonstrate the transfer learning capabilities of MobileDenseNeXt. The recommended models achieved over 95% test classification accuracy on the used three datasets, unequivocally demonstrating the high image classification proficiency of the proposed MobileDenseNeXt. Moreover, to show general classification ability of the proposed model, MobileDenseNeXt was trained on the CIFAR10 dataset and reached 98.62% accuracy. This research not only highlights the efficiency and effectiveness of MobileDenseNeXt in biomedical image classification but also highlights the competitive potential of this model for computer vision. [ABSTRACT FROM AUTHOR]

Background: Ankylosing spondylitis (AS) is a chronic, painful, progressive disease usually seen in the spine. Traditional diagnostic methods have limitations in detecting the early stages of AS. The early diagnosis of AS can improve patients’ quality of life. This study aims to diagnose AS with a pre-trained hybrid model using magnetic resonance imaging (MRI). Materials and Methods: In this research, we collected a new MRI dataset comprising three cases. Furthermore, we introduced a novel deep feature engineering model. Within this model, we utilized three renowned pretrained convolutional neural networks (CNNs): DenseNet201, ResNet50, and ShuffleNet. Through these pretrained CNNs, deep features were generated using the transfer learning approach. For each pretrained network, two feature vectors were generated from an MRI. Three feature selectors were employed during the feature selection phase, amplifying the number of features from 6 to 18 (calculated as 6 × 3). The k-nearest neighbors (kNN) classifier was utilized in the classification phase to determine classification results. During the information phase, the iterative majority voting (IMV) algorithm was applied to secure voted results, and our model selected the output with the highest classification accuracy. In this manner, we have introduced a self-organized deep feature engineering model. Results: We have applied the presented model to the collected dataset. The proposed method yielded 99.80%, 99.60%, 100%, and 99.80% results for accuracy, recall, precision, and F1-score for the collected axial images dataset. The collected coronal image dataset yielded 99.45%, 99.20%, 99.70%, and 99.45% results for accuracy, recall, precision, and F1-score, respectively. As for contrast-enhanced images, accuracy of 95.62%, recall of 80.72%, precision of 94.24%, and an F1-score of 86.96% were attained. Conclusions: Based on the results, the proposed method for classifying AS disease has demonstrated successful outcomes using MRI. The model has been tested on three cases, and its consistently high classification performance across all cases underscores the model’s general robustness. Furthermore, the ability to diagnose AS disease using only axial images, without the need for contrast-enhanced MRI, represents a significant advancement in both healthcare and economic terms.

• A new MS image dataset was collected and publicly published. • We have presented a new deep feature engineering framework to get more results. • Transfer-transfer (TT) is presented to get higher classification performances. • Our presented MSNet and TTNet have been used together. • This hybrid model achieved over 97% classification performance. Multiple sclerosis (MS) is a commonly seen neurodegenerative disorder, and early diagnosis of MS is a crucial issue to promote patient health. Since MS diagnosis is a computer vision problem, machine learning can be utilized for this purpose. Important research has used transfer learning (TL) to rapidly apply the advantages of deep learning models. Therefore, TL has developed a wide usage in computer vision applications. Herein, we describe a new algorithm in this regard, termed transfer-transfer (TT). To implement the algorithm, a multi-result machine learning model is required. In order to determine efficacy, we use transfer learning-based and hybrid feature engineering. The goal is to demonstrate the classifiability of the TT model. A new magnetic resonance image dataset containing three classes were collected to obtain TT model results, i.e.: (1) multiple sclerosis (MS), (2) myelitis, and (3) control patients. We have designed this model for MS detection. Thus, we named it MSNet. For deep feature engineering, MSNet with two layers of pretrained DenseNet201 and two layers of ResNet50 was incorporated into the system since these networks are highly accurate. By deploying these four layers, four feature vectors were calculated. ReliefF (RF), Chi2, and Neighborhood Component Analysis (NCA) were utilized in the feature selection phase, and the number of the feature vectors is increased from 4 to 12 (=4 × 3). By using k-nearest neighbor (kNN) and support vector machine (SVM) classifiers, 24 (=12 × 2) outputs were calculated, with the best result created by applying information fusion. TT incorporates the information fusion findings to construct a new feature vector. The most salient features were selected by deploying an iterative feature selector, and the features chosen were then classified. The TT-based MSNet was applied to the magnetic resonance (MR) image dataset, yielding a 97.63% classification accuracy. The findings and computed results demonstrate that the TT model with MSNet outperforms existing systems with increased transfer learning classifiability. [ABSTRACT FROM AUTHOR]