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A three-branch deep neural network for diagnosing respiratory sounds

Neural Computing and Applications 2024年第35期36卷 22611-22631页

作者： Imani, Maryam Ghassemian, Hassan Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tarbiat Modares University Tehran Iran

Finding an accurate model is essential for classification of respiratory pathologies through extraction and fusion of respiratory sounds’ features. To handle the unlabeled data, a sequence of autoencoders are used for data augmentation. A deep neural network framework is proposed to extract three types of features: 1-Features based on the human auditory system, using Mel-frequency cepstral coefficients (MFCCs), 2-Temporal features contained in the sequence of sound signal using the long short-term memory (LSTM) network, and 3-The nonlinear and complex relationship among temporal characteristics in neighborhood regions using a two-dimensional (2D) convolutional neural network (CNN) applied to the sound sequence converted to a 2D time array. Three branches are fused through some fully connected layers. The ICBHI 2017 sound database is used in two cases of 6 and 3 pathological classes. In the case of 6-classes database, 98.72% overall accuracy, 98.46% kappa coefficient, 99.66% sensitivity, 98.70% specificity and 99.70% F1-Sscore is provided. In the case of 3-classes database, 97.18% overall accuracy, 95.77% kappa coefficient, 98.31% sensitivity, 99.16% specificity and 98.93% F1-score are achieved. In contrast to the many researchers that are applying CNN on the Mel spectrogram of the audios, CNN is applied to the 2D form of the time series to find the hidden relationship among the time instants in local regions. Moreover, fusion of these hidden features with the LSTM and MFCC features leads to an accurate multiple classification. This work proposes a new deep learning framework for fusion of three types of sound features, which leads to a significant improvement in multiple respiratory infection diagnosing. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Long short-term memory

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Unsupervised Hyperspectral image Classification: Spatial and Spectral Feature Fusion with Masked Autoencoders 20

Unsupervised Hyperspectral Image Classification: Spatial and...

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20th CSI International Symposium on Artificial Intelligence and Signal processing, AISP 2024

作者： Mirsharji, Seyed Ali Ghassemian, Hassan Tarbiat Modares University Image Processing and Information Analysis Lab. Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350383942

In this study, we present an innovative unsupervised hyperspectral image classification method using a dual-branch architecture that merges spatial and spectral feature extraction. Our unique approach employs masked autoencoders, significantly outperforming traditional methods with an impressive overall accuracy of 97.1%. The paper details the model's performance evaluation, offers visual insights into its classification capabilities, and compares it with existing techniques, demonstrating its effectiveness and potential for advancing remote sensing applications. © 2024 IEEE.

关键词： image classification

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Unsupervised Change Detection in SAR images Using a Six-Branch CNN and Adaptive Window Approach 31

Unsupervised Change Detection in SAR Images Using a Six-Bran...

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31st International Conference on Electrical Engineering, ICEE 2023

作者： Kakoolvand, Abbas Imani, Maryam Ghassemian, Hassan Tarbiat Modares University Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350312560

Change detection is one of the important and hot topics in remote sensing. Adaptive windowing approaches can preserve image details while reduce noise in the process of change detection. In the proposed method, two difference images (DIs) are obtained by using the adaptive window approach. Some fake labels are provided from these DIs. Neural networks have a good performance in image processing. To use this advantage, a six-branch convolutional neural network (CNN) is trained using the fake labels. Using these six branches with utilizing the adaptive windows, the network can preserve image detail while reduce noise. The proposed method based on three criteria (PCC, Kappa, and F1 score) has the best results in two datasets and has results close to the best method in other datasets. © 2023 IEEE.

关键词： Synthetic aperture radar

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Fusion of Multi-Level CNN With LBP Features For Facial Emotion Recognition 31

Fusion of Multi-Level CNN With LBP Features For Facial Emoti...

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31st International Conference on Electrical Engineering, ICEE 2023

作者： Bahmanabady, Ehsan Imani, Maryam Ghassemian, Hassan Tarbiat Modares University Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9798350312560

A facial emotion recognition framework is proposed in this work. The convolutional neural network (CNN) has high ability in extraction of hierarchical spatial features from low level texture characteristics to high level contextual features. A simple CNN model with three layers is suggested in this paper where the features extracted in all layers containing multi-level features are activated and used for classification. In addition, the local binary pattern (LBP) descriptor is used to extract discriminative features from the spatial structure of the input image. Therefore, four feature sources are provided by multi levels of CNN and the LBP descriptor. Each feature source is used for facial emotion recognition by applying to the support vector machine (SVM) classier. Finally, the majority voting rule is used for decision fusion to provide the final emotional label of each given face image. The proposed method with 84% overall accuracy, 83% weighted F1-score and 81% kappa coefficient provides the best performance compared to LBP, multi-level CNN and two-dimensional principal component analysis (2DPCA) methods. © 2023 IEEE.

关键词： Emotion Recognition

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Building Detection Using Very High Resolution SAR images with Multi-Direction Based on Weighted-Morphological Indexes 12

Building Detection Using Very High Resolution SAR Images wit...

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12th Iranian/2nd International Conference on Machine Vision and image processing, MVIP 2022

作者： Amjadipour, Fateme Ghassemian, Hassan Imani, Maryam Tarbiat Modares University Image Processing and Information Analysis Lab Tehran Iran

ISBN: (纸本)9781665412162

Today, technological advancement in production of radar images can be seen with high spatial resolution and also the availability of these images' significant growth in interpretation and processing of high-resolution radar images. The building extraction from urban areas is one of the most challenging applications in VHR SAR image, which is used to estimate the population and urban development. Detection of individual buildings in the urban context is highly considered by researchers due to complexity of interpreting radar images in these fields. On the other hand, one of the main issues in the complexity of the scatters received from buildings is change in direction of the building relative to the horizon, which is correlated with the look angle. Other influential parameters are geometric distortions, which include layover and shadow effects. In some cases, the effect of shadow is an auxiliary parameter in detection of these targets that increases accuracy of the detection. In this paper, we intend to extract the building from high spatial resolution SAR images using fuzzy fusion of two morphological indicators, SI and DI, which represent the shadow and bright area, respectively. Due to the effect of SAR imaging geometry on ground targets, different sizes and directions of structural elements were applied to the image. The use of indicators weights with different sizes is proposed in this work. The Detection Ratio of experiment of TerraSAR-X image has a result of 95.3%. © 2022 IEEE.

关键词： Morphology

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Accurate delineation of multiple CT liver lesions by deep processing of image patches 24

Accurate delineation of multiple CT liver lesions by deep pr...

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7th International Conference on Machine Learning and Machine Intelligence, MLMI 2024

作者： Javadi, Alireza Foruzan, Amir Hossein Chen, Yen-Wei Department of Biomedical Engineering Engineering Faculty Shahed University Tehran Iran Intelligent Image Processing Lab College of Information Science and Engineering Ritsumeikan University Shiga Japan

ISBN: (纸本)9798400717833

One of the primary causes of death is liver cancer-related liver abnormalities. Finding a trustworthy treatment plan needs an early diagnosis of liver abnormalities. Available convolutional neural networks cannot identify the region of interest and the number of lesions in a slice in CT images. Furthermore, there is a challenge in the detection of small lesions. To address the abovementioned problems, this paper presents a novel approach for liver lesion detection using the You Only Look Ones (Yolo) 8 network. It proposes a preprocessing method for dividing each slice into overlapping small patches. We used four metrics to validate the performance of our method: precision, recall, F1-score, and hit-and-miss ratio. On the 3DirCADb public dataset, the proposed method achieved 97%, 78%, 87%, and 82% concerning precision, recall, F1-score, and hit-and-miss ratio. © 2024 Copyright held by the owner/author(s).

关键词： Convolutional neural networks

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Deep Curriculum Learning for PolSAR image Classification 12

Deep Curriculum Learning for PolSAR Image Classification

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12th Iranian/2nd International Conference on Machine Vision and image processing, MVIP 2022

作者： Mousavi, Hamidreza Imani, Maryam Ghassemian, Hassan Image Processing and Information Analysis Lab Faculty of Electrical and Computer Engineering Tehran Iran

ISBN: (纸本)9781665412162

Following the great success of curriculum learning in the area of machine learning, a novel deep curriculum learning method proposed in this paper, entitled DCL, particularly for the classification of fully polarimetric synthetic aperture radar (PolSAR) data. This method utilizes the entropy-alpha target decomposition method to estimate the degree of complexity of each PolSAR image patch before applying it to the convolutional neural network (CNN). Also, an accumulative mini-batch pacing function is used to introduce more difficult patches to CNN. Experiments on the widely used data set of AIRSAR Flevoland reveal that the proposed curriculum learning method can not only increase classification accuracy but also lead to faster training convergence. © 2022 IEEE.

关键词： Synthetic aperture radar

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Highresolution Remote Sensing image Classification With Limited Training Data

Highresolution Remote Sensing Image Classification With Limi...

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Iranian Machine Vision and image processing (MVIP)

作者： Mehdi Ariaei Hassan Ghassemian Maryam Imani Image Processing and Information Analysis Lab Tarbiat Modares University Tehran Iran

Accurate classification of land cover from aerial images is one of the research topics in remote sensing and is also in high demand in industry. However, obtaining labeled data for training different classifiers that heavily depend on supervision is still a challenging and resource-intensive task. Unsupervised methods have emerged as a powerful alternative to overcome the limitations associated with labeled data. Such methods have a high ability to discover hidden patterns and structures in multi-spectral images and have the possibility of classifying various types of land cover without relying on labeled samples. Our research primarily involved the analysis of World-View3 satellite imagery. Our strategy involved creating an advanced pipeline that extracted features using autoencoders. Through this approach, the multispectral images' key characteristics are efficiently extracted. Subsequently, we implement transfer learning to re-train the model with a limited number of labeled data. By applying transfer learning, our pipeline significantly enhances the capability of multispectral image processing, enabling a more comprehensive and accurate interpretation of satellite imagery data. Finally, we evaluate our results not only by providing a confusion matrix but also through a visual comparison between the class map and the RGB composition of the MSI image.

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Sign Language Recognition using Spiking Neural Networks 2

Sign Language Recognition using Spiking Neural Networks

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2nd International Conference on Machine Learning and Data Engineering, ICMLDE 2023

作者： Chaudhari, Pranav Vicente-Sola, Alex Basu, Amlan Manna, Davide L. Kirkland, Paul Caterina, Gaetano Di Department of Information and Communication Technology Manipal Institute of Technology Manipal Academy of Higher Education Manipal576104 India Neuromorphic Sensor Signal Processing Lab Centre for Image and Signal Processing Electrical and Electronic Engineering Department University of Strathclyde Glasgow United Kingdom

In recent years, research in automatic Sign Language Recognition (SLR) has undergone significant progress, serving as a founda-tional base for developing applications that aim to promote the integration of deaf individuals into society. Most of this progress is owed to the recent developments in deep learning. However, the deployment of conventional Artificial Neural Networks (ANNs) can be hindered by their requirements in terms of computational power and energy consumption. Therefore, to improve the ef-ciency of current SLR systems, in this work, we propose the use of the increasingly popular Spiking Neural Networks (SNNs), which, on the one hand, provide more energy-efficient computations than conventional ANNs and, on the other hand, are able to process temporal sequences with simpler architectures thanks to their temporal dynamics. To evaluate our method, we utilize WLASL300, the 300-word (300 classes of signs) dataset from Word-Level American Sign Language, and achieve an improvement in accuracy with the SNN (+2.70%) over the previous state-of-the-art, when working with energy-efficient spiking neurons. Furthermore, we construct a non-spiking version of the same network and evaluate it in a similar manner. Our results demonstrate how the SNN has sparser activations (25% less), thanks to the use of spiking neurons, and therefore can be implemented with a lower power requirement than an ANN version of the same architecture. This work thus demonstrates the possibility of performing SLR in a very effective and efficient way, thus opening up the development of applications that span from the automatic real-time translation of dynamic signs to remote control utilizing sign languages. © 2024 Elsevier B.V.. All rights reserved.

关键词： Neural networks

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Analysis of multimodal fusion strategies in deep learning for ischemic stroke lesion segmentation on computed tomography perfusion data

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Multimedia Tools and Applications 2025年第10期84卷 7493-7518页

作者： Raju, Chintha Sri Pothu Neelapu, Bala Chakravarthy Laskar, Rabul Hussain Muhammad, Ghulam Speech and Image Processing Lab Department of ECE National Institute of Technology Silchar Assam Silchar788010 India Department of Biotechnology and Medical Engineering National Institute of Technology Rourkela Odisha Rourkela769008 India Department of Computer Engineering College of Computer and Information Sciences King Saud University Riyadh11543 Saudi Arabia

Stroke poses a significant risk to human life. Segmenting and immediately treating the stroke core stops its further development, therefore, enhancing the likelihood of survival. Convolutional neural networks (CNN) have been very successful in medical image segmentation, namely in the field of deep learning, and have produced the most advanced outcomes. Multi-modal images provide superior outcomes in the segmentation of stroke lesions compared to single-modal images. The integration of input from several modalities at various levels is crucial in determining performance and producing diverse outcomes in deep learning models that use multimodalities. Further investigation is required to explore the optimal methods for processing multimodal data in CNNs, the influence of fusion on CNN learning, and the effect of fusion strategies on lesions of varying sizes. To examine the impact of a multi-modal fusion method on lesion segmentation, we assessed four models using distinct fusion techniques, including early, late, bottleneck, and hierarchical fusions. This study discusses the various fusion procedures used in segmenting the lesion using computed tomography perfusion data. In addition, both quantitative and qualitative assessments, including deep feature analysis and feature similarity, were conducted to assess the impact of the fusion technique on the model’s performance. Furthermore, we examined the influence of fusion techniques on the size of the lesion. In addition, we analyzed the advantages and disadvantages of several multi-modal fusion systems. Our findings demonstrate that the bottleneck fusion technique got the highest dice score, 0.582, on the Ischemic Stroke Lesion Segmentation 2018 validation data as a result of its capacity to construct complex relationships across several modalities. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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