TReC: Transferred ResNet and CBAM for Detecting Brain Diseases

Early diagnosis of pathological brains leads to early interventions in brain diseases, which may help control the illness conditions, prolong the life of patients, and even cure them. Therefore, the classification of brain diseases is a challenging but helpful task. However, it is hard to collect brain images, and the superabundance of images is also a great challenge for computing resources. This study proposes a new approach named TReC: Transferred Residual Networks (ResNet)-Convolutional Block Attention Module (CBAM), a specific model for small-scale samples, to detect brain diseases based on MRI. At first, the ResNet model, which is pre-trained on the ImageNet dataset, serves as initialization. Subsequently, a simple attention mechanism named CBAM is introduced and added into every ResNet residual block. At the same time, the fully connected (FC) layers of the ResNet are replaced with new FC layers, which meet the goal of classification. Finally, all the parameters of our model, such as the ResNet, the CBAM, and new FC layers, are retrained. The effectiveness of the proposed model is evaluated on brain magnetic resonance (MR) datasets for multi-class and two-class tasks. Compared with other state-of-the-art models, our model reaches the best performance for two-class and multi-class tasks on brain diseases.

Download Full-text

A Novel Architecture to Classify Histopathology Images Using Convolutional Neural Networks

Applied Sciences ◽

10.3390/app10082929 ◽

2020 ◽

Vol 10 (8) ◽

pp. 2929 ◽

Cited By ~ 2

Author(s):

Ibrahem Kandel ◽

Mauro Castelli

Keyword(s):

Neural Network ◽

Neural Networks ◽

State Of The Art ◽

Treatment Plan ◽

Tissue Structure ◽

Activation Functions ◽

Proposed Model ◽

Histopathology Images ◽

Fully Connected

Histopathology is the study of tissue structure under the microscope to determine if the cells are normal or abnormal. Histopathology is a very important exam that is used to determine the patients’ treatment plan. The classification of histopathology images is very difficult to even an experienced pathologist, and a second opinion is often needed. Convolutional neural network (CNN), a particular type of deep learning architecture, obtained outstanding results in computer vision tasks like image classification. In this paper, we propose a novel CNN architecture to classify histopathology images. The proposed model consists of 15 convolution layers and two fully connected layers. A comparison between different activation functions was performed to detect the most efficient one, taking into account two different optimizers. To train and evaluate the proposed model, the publicly available PatchCamelyon dataset was used. The dataset consists of 220,000 annotated images for training and 57,000 unannotated images for testing. The proposed model achieved higher performance compared to the state-of-the-art architectures with an AUC of 95.46%.

Download Full-text

Image-based state-of-the-art techniques for the identification and classification of brain diseases: a review

Medical & Biological Engineering & Computing ◽

10.1007/s11517-020-02256-z ◽

2020 ◽

Vol 58 (11) ◽

pp. 2603-2620

Author(s):

Ejaz Ul Haq ◽

Jianjun Huang ◽

Li Kang ◽

Hafeez Ul Haq ◽

Tijiang Zhan

Keyword(s):

State Of The Art ◽

Brain Diseases ◽

Art Techniques

Download Full-text

ECG Heartbeat Classification Based on an Improved ResNet-18 Model

Computational and Mathematical Methods in Medicine ◽

10.1155/2021/6649970 ◽

2021 ◽

Vol 2021 ◽

pp. 1-13

Author(s):

Enbiao Jing ◽

Haiyang Zhang ◽

ZhiGang Li ◽

Yazhi Liu ◽

Zhanlin Ji ◽

...

Keyword(s):

State Of The Art ◽

Classification Performance ◽

Classification Models ◽

Heartbeat Classification ◽

Ecg Signals ◽

Residual Structure ◽

Proposed Model ◽

Model Training ◽

Electrocardiogram Ecg

Based on a convolutional neural network (CNN) approach, this article proposes an improved ResNet-18 model for heartbeat classification of electrocardiogram (ECG) signals through appropriate model training and parameter adjustment. Due to the unique residual structure of the model, the utilized CNN layered structure can be deepened in order to achieve better classification performance. The results of applying the proposed model to the MIT-BIH arrhythmia database demonstrate that the model achieves higher accuracy (96.50%) compared to other state-of-the-art classification models, while specifically for the ventricular ectopic heartbeat class, its sensitivity is 93.83% and the precision is 97.44%.

Download Full-text

Image-Based Malware Classification Using VGG19 Network and Spatial Convolutional Attention

Electronics ◽

10.3390/electronics10192444 ◽

2021 ◽

Vol 10 (19) ◽

pp. 2444

Author(s):

Mazhar Javed Awan ◽

Osama Ahmed Masood ◽

Mazin Abed Mohammed ◽

Awais Yasin ◽

Azlan Mohd Zain ◽

...

Keyword(s):

High Performance ◽

State Of The Art ◽

Malware Detection ◽

Detection Methods ◽

Learning Framework ◽

Malware Classification ◽

Proposed Model ◽

Communication Devices ◽

Digital Assets

In recent years the amount of malware spreading through the internet and infecting computers and other communication devices has tremendously increased. To date, countless techniques and methodologies have been proposed to detect and neutralize these malicious agents. However, as new and automated malware generation techniques emerge, a lot of malware continues to be produced, which can bypass some state-of-the-art malware detection methods. Therefore, there is a need for the classification and detection of these adversarial agents that can compromise the security of people, organizations, and countless other forms of digital assets. In this paper, we propose a spatial attention and convolutional neural network (SACNN) based on deep learning framework for image-based classification of 25 well-known malware families with and without class balancing. Performance was evaluated on the Malimg benchmark dataset using precision, recall, specificity, precision, and F1 score on which our proposed model with class balancing reached 97.42%, 97.95%, 97.33%, 97.11%, and 97.32%. We also conducted experiments on SACNN with class balancing on benign class, also produced above 97%. The results indicate that our proposed model can be used for image-based malware detection with high performance, despite being simpler as compared to other available solutions.

Download Full-text

Fusion Based AER System Using Deep Learning Approach for Amplitude and Frequency Analysis

ACM Transactions on Asian and Low-Resource Language Information Processing ◽

10.1145/3488369 ◽

2022 ◽

Vol 21 (3) ◽

pp. 1-19

Author(s):

A. Pramod Reddy ◽

Vijayarajan V.

Keyword(s):

State Of The Art ◽

Deep Understanding ◽

Image Features ◽

Frequency Scale ◽

Proposed Model ◽

Fused Image ◽

Near Term ◽

Fully Connected ◽

Frequency Features ◽

Fine Tune

Automatic emotion recognition from Speech (AERS) systems based on acoustical analysis reveal that some emotional classes persist with ambiguity. This study employed an alternative method aimed at providing deep understanding into the amplitude–frequency, impacts of various emotions in order to aid in the advancement of near term, more effectively in classifying AER approaches. The study was undertaken by converting narrow 20 ms frames of speech into RGB or grey-scale spectrogram images. The features have been used to fine-tune a feature selection system that had previously been trained to recognise emotions. Two different Linear and Mel spectral scales are used to demonstrate a spectrogram. An inductive approach for in sighting the amplitude and frequency features of various emotional classes. We propose a two-channel profound combination of deep fusion network model for the efficient categorization of images. Linear and Mel- spectrogram is acquired from Speech-signal, which is prepared in the recurrence area to input Deep Neural Network. The proposed model Alex-Net with five convolutional layers and two fully connected layers acquire most vital features form spectrogram images plotted on the amplitude-frequency scale. The state-of-the-art is compared with benchmark dataset (EMO-DB). RGB and saliency images are fed to pre-trained Alex-Net tested both EMO-DB and Telugu dataset with an accuracy of 72.18% and fused image features less computations reaching to an accuracy 75.12%. The proposed model show that Transfer learning predict efficiently than Fine-tune network. When tested on Emo-DB dataset, the propȯsed system adequately learns discriminant features from speech spectrȯgrams and outperforms many stȧte-of-the-art techniques.

Download Full-text

Histopathological Classification of Breast Cancer Images Using a Multi-Scale Input and Multi-Feature Network

Cancers ◽

10.3390/cancers12082031 ◽

2020 ◽

Vol 12 (8) ◽

pp. 2031 ◽

Cited By ~ 2

Author(s):

Taimoor Shakeel Sheikh ◽

Yonghee Lee ◽

Migyung Cho

Keyword(s):

State Of The Art ◽

Texture Features ◽

Feature Maps ◽

Histopathological Classification ◽

Multi Scale ◽

Machine Learning Methods ◽

Proposed Model ◽

Benchmark Datasets ◽

Histopathological Images

Diagnosis of pathologies using histopathological images can be time-consuming when many images with different magnification levels need to be analyzed. State-of-the-art computer vision and machine learning methods can help automate the diagnostic pathology workflow and thus reduce the analysis time. Automated systems can also be more efficient and accurate, and can increase the objectivity of diagnosis by reducing operator variability. We propose a multi-scale input and multi-feature network (MSI-MFNet) model, which can learn the overall structures and texture features of different scale tissues by fusing multi-resolution hierarchical feature maps from the network’s dense connectivity structure. The MSI-MFNet predicts the probability of a disease on the patch and image levels. We evaluated the performance of our proposed model on two public benchmark datasets. Furthermore, through ablation studies of the model, we found that multi-scale input and multi-feature maps play an important role in improving the performance of the model. Our proposed model outperformed the existing state-of-the-art models by demonstrating better accuracy, sensitivity, and specificity.

Download Full-text

EnCNN-UPMWS: Waste Classification by a CNN Ensemble Using the UPM Weighting Strategy

Electronics ◽

10.3390/electronics10040427 ◽

2021 ◽

Vol 10 (4) ◽

pp. 427

Author(s):

Hua Zheng ◽

Yu Gu

Keyword(s):

Ensemble Learning ◽

State Of The Art ◽

Learning Model ◽

Predicted Probability ◽

Proposed Model ◽

Potential Applications ◽

Weighting Strategy ◽

Validation Set ◽

Weight Coefficients

The accurate and effective classification of household solid waste (HSW) is an indispensable component in the current procedure of waste disposal. In this paper, a novel ensemble learning model called EnCNN-UPMWS, which is based on convolutional neural networks (CNNs) and an unequal precision measurement weighting strategy (UPMWS), is proposed for the classification of HSW via waste images. First, three state-of-the-art CNNs, namely GoogLeNet, ResNet-50, and MobileNetV2, are used as ingredient classifiers to separately predict and obtain three predicted probability vectors, which are significant elements that affect the prediction performance by providing complementary information about the patterns to be classified. Then, the UPMWS is introduced to determine the weight coefficients of the ensemble models. The actual one-hot encoding labels of the validation set and the predicted probability vectors from the CNN ensemble are creatively used to calculate the weights for each classifier during the training phase, which can bring the aggregated prediction vector closer to the target label and improve the performance of the ensemble model. The proposed model was applied to two datasets, namely TrashNet (an open-access dataset) and FourTrash, which was constructed by collecting a total of 47,332 common HSW images containing four types of waste (wet waste, recyclables, harmful waste, and dry waste). The experimental results demonstrate the effectiveness of the proposed method in terms of its accuracy and F1-scores. Moreover, it was found that the UPMWS can simply and effectively enhance the performance of the ensemble learning model, and has potential applications in similar tasks of classification via ensemble learning.

Download Full-text

Detection and Classification of Different Weapon Types Using Deep Learning

Applied Sciences ◽

10.3390/app11167535 ◽

2021 ◽

Vol 11 (16) ◽

pp. 7535

Author(s):

Volkan Kaya ◽

Servet Tuncer ◽

Ahmet Baran

Keyword(s):

Deep Learning ◽

Control Systems ◽

Training Process ◽

Security Forces ◽

New Approach ◽

New Model ◽

Automatic Control Systems ◽

Proposed Model ◽

Computer Environment

Today, with the increasing number of criminal activities, automatic control systems are becoming the primary need for security forces. In this study, a new model is proposed to detect seven different weapon types using the deep learning method. This model offers a new approach to weapon classification based on the VGGNet architecture. The model is taught how to recognize assault rifles, bazookas, grenades, hunting rifles, knives, pistols, and revolvers. The proposed model is developed using the Keras library on the TensorFlow base. A new model is used to determine the method required to train, create layers, implement the training process, save training in the computer environment, determine the success rate of the training, and test the trained model. In order to train the model network proposed in this study, a new dataset consisting of seven different weapon types is constructed. Using this dataset, the proposed model is compared with the VGG-16, ResNet-50, and ResNet-101 models to determine which provides the best classification results. As a result of the comparison, the proposed model’s success accuracy of 98.40% is shown to be higher than the VGG-16 model with 89.75% success accuracy, the ResNet-50 model with 93.70% success accuracy, and the ResNet-101 model with 83.33% success accuracy.

Download Full-text

Classification of tectonic and non-tectonic seismicity based on convolutional neural network

Geophysical Journal International ◽

10.1093/gji/ggaa444 ◽

2020 ◽

Vol 224 (1) ◽

pp. 191-198

Author(s):

Xinliang Liu ◽

Tao Ren ◽

Hongfeng Chen ◽

Yufeng Chen

Keyword(s):

Neural Network ◽

Neural Networks ◽

Convolutional Neural Network ◽

Convolutional Neural Networks ◽

Proposed Model ◽

Single Station ◽

Using Data ◽

Fully Connected

SUMMARY In this paper, convolutional neural networks (CNNs) were used to distinguish between tectonic and non-tectonic seismicity. The proposed CNNs consisted of seven convolutional layers with small kernels and one fully connected layer, which only relied on the acoustic waveform without extracting features manually. For a single station, the accuracy of the model was 0.90, and the event accuracy could reach 0.93. The proposed model was tested using data from January 2019 to August 2019 in China. The event accuracy could reach 0.92, showing that the proposed model could distinguish between tectonic and non-tectonic seismicity.

Download Full-text

Joint Detection and Classification of Singing Voice Melody Using Convolutional Recurrent Neural Networks

Applied Sciences ◽

10.3390/app9071324 ◽

2019 ◽

Vol 9 (7) ◽

pp. 1324 ◽

Cited By ~ 2

Author(s):

Sangeun Kum ◽

Juhan Nam

Keyword(s):

Loss Function ◽

State Of The Art ◽

Joint Detection ◽

Singing Voice ◽

Pitch Estimation ◽

Proposed Model ◽

Pitch Contours ◽

Multi Level ◽

Extraction Performance

Singing melody extraction essentially involves two tasks: one is detecting the activity of a singing voice in polyphonic music, and the other is estimating the pitch of a singing voice in the detected voiced segments. In this paper, we present a joint detection and classification (JDC) network that conducts the singing voice detection and the pitch estimation simultaneously. The JDC network is composed of the main network that predicts the pitch contours of the singing melody and an auxiliary network that facilitates the detection of the singing voice. The main network is built with a convolutional recurrent neural network with residual connections and predicts pitch labels that cover the vocal range with a high resolution, as well as non-voice status. The auxiliary network is trained to detect the singing voice using multi-level features shared from the main network. The two optimization processes are tied with a joint melody loss function. We evaluate the proposed model on multiple melody extraction and vocal detection datasets, including cross-dataset evaluation. The experiments demonstrate how the auxiliary network and the joint melody loss function improve the melody extraction performance. Furthermore, the results show that our method outperforms state-of-the-art algorithms on the datasets.

Download Full-text