Classification of medical images based on deep stacked patched auto-encoders

Ali, Ramzi Ben; Ejbali, Ridha; Zaied, Mourad

doi:10.1007/s11042-020-09056-5

Cited by 5 publications

(4 citation statements)

References 35 publications

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“…An et al (2021) proposed a multi-scale convolutional neural network, a medical classification algorithm based on a visual attention mechanism, which automatically extracts highlevel discriminative appearance features from the original image. In the method proposed by Ben et al (2020), a new classification framework was developed to classify medical images using sparse coding and wavelet analysis, which showed a significant improvement in identification accuracy. Cheng et al (2022) FIGURE 14 | Performance comparison with various models.…”

Section: Discussionmentioning

confidence: 99%

3D FRN-ResNet: An Automated Major Depressive Disorder Structural Magnetic Resonance Imaging Data Identification Framework

Hong

Huang

et al. 2022

Front. Aging Neurosci.

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Major Depressive Disorder (MDD) is the most prevalent psychiatric disorder, seriously affecting people’s quality of life. Manually identifying MDD from structural magnetic resonance imaging (sMRI) images is laborious and time-consuming due to the lack of clear physiological indicators. With the development of deep learning, many automated identification methods have been developed, but most of them stay in 2D images, resulting in poor performance. In addition, the heterogeneity of MDD also results in slightly different changes reflected in patients’ brain imaging, which constitutes a barrier to the study of MDD identification based on brain sMRI images. We propose an automated MDD identification framework in sMRI data (3D FRN-ResNet) to comprehensively address these challenges, which uses 3D-ResNet to extract features and reconstruct them based on feature maps. Notably, the 3D FRN-ResNet fully exploits the interlayer structure information in 3D sMRI data and preserves most of the spatial details as well as the location information when converting the extracted features into vectors. Furthermore, our model solves the feature map reconstruction problem in closed form to produce a straightforward and efficient classifier and dramatically improves model performance. We evaluate our framework on a private brain sMRI dataset of MDD patients. Experimental results show that the proposed model exhibits promising performance and outperforms the typical other methods, achieving the accuracy, recall, precision, and F1 values of 0.86776, 0.84237, 0.85333, and 0.84781, respectively.

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Section: Discussionmentioning

confidence: 99%

3D FRN-ResNet: An Automated Major Depressive Disorder Structural Magnetic Resonance Imaging Data Identification Framework

Hong

Huang

et al. 2022

Front. Aging Neurosci.

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“…ResNet18 is used as the core module of the developed model, as is shown in Fig. In order to evaluate the performance of different machine learning methods, accuracy, specificity and sensitivity are used as metrics, meanwhile the performance of the deep learning method proposed is compared with that of the autoencoder method [12] and the neural network method [13].…”

Section: Deep Learning Methodsmentioning

confidence: 99%

Deep learning for differentiating benign from malignant tumors on breast-specific gamma image

Lei

Dong

Yang

et al. 2023

THC

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BACKGROUND: Breast diseases are a significant health threat for women. With the fast-growing BSGI data, it is becoming increasingly critical for physicians to accurately diagnose benign as well as malignant breast tumors. OBJECTIVE: The purpose of this study is to diagnose benign and malignant breast tumors utilizing the deep learning model, with the input of breast-specific gamma imaging (BSGI). METHODS: A benchmark dataset including 144 patients with benign tumors and 87 patients with malignant tumors was collected and divided into a training dataset and a test dataset according to the ratio of 8:2. The convolutional neural network ResNet18 was employed to develop a new deep learning model. The model proposed was compared with neural network and autoencoder models. Accuracy, specificity, sensitivity and ROC were used to evaluate the performance of different models. RESULTS: The accuracy, specificity and sensitivity of the model proposed are 99.1%, 98.8% and 99.3% respectively, which achieves the best performance among all methods. Additionally, the Grad-CAM method is used to analyze the interpretability of the diagnostic results based on the deep learning model. CONCLUSION: This study demonstrates that the proposed deep learning method could help physicians diagnose benign and malignant breast tumors quickly as well as reliably.

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“…Once the pre-processing step has been conducted, the Fast Beta Wavelet Network (FBWN) analysis is exploited for the rapid extraction of appropriate shape, texture and color features from the egg images. The FBWN success has been demonstrated in a variety works for different applications [19], [24], [25]. A FBWN is a type of neural network that uses wavelet transforms as its basic building blocks to extract the most important features that allow the reconstruction of the input data [8], [19], [26], [27].…”

Section: B Fast Beta Wavelet Network For Feature Extractionmentioning

confidence: 99%

Deep Learning-Based Parasitic Egg Identification From a Slender-Billed Gull’s Nest

2023

Self Cite

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Intraspecific nest parasitism is a phenomenon that attracts the attention of biologists since it helps in saving the endangered species such as Slender Billed Gull. The problem comes from the fact that a parasite female lays its eggs in the nest of another female (host) of the same species which causes the abandon of the nest by the host. This behavior causes a significant reduction in future birds number and leads to the expansion of this specie. Thus, there has been an urgent necessity to clean the nest from parasitic eggs. So, our aim is to built an automatic parasitic egg identification system based on egg visual features information. Our system uses deep learning models which have proven their success for image classification. Indeed, our system conduct an egg image's pre-processing phase followed by Fast Beta Wavelet Network (FBWN) to extract the most efficient descriptors (shape, texture, and color). Then, these features will be inputted to the Stacked AutoEncoder for egg classification. Our proposed system, has been evaluated on 91-egg dataset collected from 31 clutches of eggs in Sfax region, Tunisia. Our model has given a parasitic egg identification accuracy of 89.9% which has outperformed the state of the art method and shows the efficiency and the robustness of our system.

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Classification of medical images based on deep stacked patched auto-encoders

Cited by 5 publications

References 35 publications

3D FRN-ResNet: An Automated Major Depressive Disorder Structural Magnetic Resonance Imaging Data Identification Framework

3D FRN-ResNet: An Automated Major Depressive Disorder Structural Magnetic Resonance Imaging Data Identification Framework

Deep learning for differentiating benign from malignant tumors on breast-specific gamma image

Deep Learning-Based Parasitic Egg Identification From a Slender-Billed Gull’s Nest

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