Multi-Class brain normality and abnormality diagnosis using modified Faster R-CNN

Uyar, Kübra; Taşdemir, Şakir; Ülker, Erkan; Öztürk, Mehmet; Kasap, Hüseyin

doi:10.1016/j.ijmedinf.2021.104576

Cited by 11 publications

(3 citation statements)

References 16 publications

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“…In addition, 10 e-2 , 10 e-3 , 10 e-4 , 10 e-5 and 10 e-6 Initial learning Rate parameter values were run in LearnRateSchedule in pairs of none and piecewise. In cases where the LearnRateSchedule parameter is piecewise, the influencing LearnRateDropFactor parameter is kept constant at 0.9 and the LearnRateDropPeriod parameter is kept constant at 5 [25].…”

Section: Parameter Optimization In Alexnet Architecturementioning

confidence: 99%

Effects of Training Parameters of AlexNet Architecture on Wound Image Classification

Eldem¹,

Ülker²,

Işıklı³

2023

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Deep learning is more extensively used in image analysis-based classification of wounds with an aim to facilitate the monitoring of wound prognosis in preventive treatments. In this paper, the classification success of AlexNet architecture in pressure and diabetic foot wound images is discussed. Optimizing training parameters in order to increase the success of Convolutional Neural Network (CNN) architectures is a frequently discussed problem. This paper comparatively examines the effects of optimization of the training parameters of CNN architecture on classification success. The paper examines how the optimizer algorithm, mini-batch size (MBS), maximum epoch number (ME), learning rate (LR), and LearnRateSchedule (LRS) parameters, which are among the training parameters used in combination in architectural training, perform at different values. The best results were obtained with an accuracy of 95.48% at the 10 e-4 value of the LR parameter. When the changes in the evaluation metrics during the parameter optimization experiments were examined, it was seen that the LR parameter produced optimum values at 10 e-4 . As a result, when the Accuracy metric and standard deviations were examined, it was determined only with the LR parameter. No general conclusion could be reached regarding the other parameters.

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Section: Parameter Optimization In Alexnet Architecturementioning

confidence: 99%

Effects of Training Parameters of AlexNet Architecture on Wound Image Classification

Eldem¹,

Ülker²,

Işıklı³

2023

View full text Add to dashboard Cite

show abstract

“…Xinsheng Zhan proposed a multiple feature fusion mechanism for micro-calcified clusters in X-ray images, involving double sampling on the underlying feature map followed by horizontal connection to the previous layer [ 11 ]. Atkale et al suggested a multi-scale feature fusion model for facial aging, featuring 5 parallel branches and employing up-sampling and down-sampling operations through pooling, convolution, and cavity convolution [ 12 ]. Bakkouri et al presented a 3D multi-scale feature fusion algorithm with four levels, each comprising four 3D-CNN branches of identical architecture but different parameters [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

3D residual attention hierarchical fusion for real-time detection of the prostate capsule

Wu,

Guo,

Litifu

et al. 2024

BMC Med Imaging

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Background For prostate electrosurgery, where real-time surveillance screens are relied upon for operations, manual identification of the prostate capsule remains the primary method. With the need for rapid and accurate detection becoming increasingly urgent, we set out to develop a deep learning approach for detecting the prostate capsule using endoscopic optical images. Methods Our method involves utilizing the Simple, Parameter-Free Attention Module(SimAM) residual attention fusion module to enhance the extraction of texture and detail information, enabling better feature extraction capabilities. This enhanced detail information is then hierarchically transferred from lower to higher levels to aid in the extraction of semantic information. By employing a forward feature-by-feature hierarchical fusion network based on the 3D residual attention mechanism, we have proposed an improved single-shot multibox detector model. Results Our proposed model achieves a detection precision of 83.12% and a speed of 0.014 ms on NVIDIA RTX 2060, demonstrating its effectiveness in rapid detection. Furthermore, when compared to various existing methods including Faster Region-based Convolutional Neural Network (Faster R-CNN), Single Shot Multibox Detector (SSD), EfficientDet and others, our method Attention based Feature Fusion Single Shot Multibox Detector (AFFSSD) stands out with the highest mean Average Precision (mAP) and faster speed, ranking only below You Only Look Once version 7 (YOLOv7). Conclusions This network excels in extracting regional features from images while retaining the spatial structure, facilitating the rapid detection of medical images.

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“…The two horizontal connection layers are connected in the same spatial dimension to enhance the positioning information of the object from the bottom. Atkale et al proposed a multi-scale feature fusion model for facial aging, which includes 5 parallel branches and completes up-sampling and down-sampling operations through pooling, convolution and cavity convolution [12]. For the diagnosis of Alzheimer's disease, Bakkouri et al proposed a 3D multi-scale feature fusion algorithm, which consists of four levels, each of which contains four 3D-CNN branches with the same architecture but different parameters [13].…”

Section: Introductionmentioning

confidence: 99%

3D residual attention hierarchical fusion for real-time detection of the prostate capsula

Wu,

Guo,

Litifu

et al. 2023

Preprint

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Background: For electrosurgery of the prostate, which relies on surveillance screens for real-time operations, manual remains the primary method for prostate capsula identification, rapid and accurate detection becomes urgency.We aimed to develop a deep learning method for detecting prostate capsula using endoscopic optical images. Methods: Firstly, the SimAM residual attention fusion module is used to enhance the feature extraction ability of texture and detail informations. Secondly, the enhanced details information at the lower level is transferred to the higher level in a hierarchical manner to facilitate the extraction of semantic information. Finally, based on SimAM residual attention and forward hierarchical feature-by-feature fusion, an improved single-shot multibox detector model based on 3D unparametric attention fusion is proposed. Results: A forward feature-by-feature hierarchical fusion network based on the 3D residual attention mechanism is proposed for the fast detection of the prostate capsula. The proposed network can better extract the regional features of the image, the extracted network features retain the spatial structure of the image, and realize the rapid detection of medical images. Conclusions: The speed of the proposed model can reach 0.014ms on NVIDIA RTX 2060, which realizes the rapid detection. The model AFFSSD composed of unparametric attention fusion and progressive fusion of forward features can achieve 83.12% detection precision. Compared with Faster R-CNN (ZF, VGG16, ResNet 50), SSD(VGG16, ResNet 101), EfficientDet (D0-D7), FoveaBox, TOOD, YOLOv4, Sparse R-CNN, OWOD, .R-FCN(ResNet-50), FSSD(VGG16), the proposed method AFFSSD had the highest mAP and faster speed, only lower than YOLOv7.

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Multi-Class brain normality and abnormality diagnosis using modified Faster R-CNN

Cited by 11 publications

References 16 publications

Effects of Training Parameters of AlexNet Architecture on Wound Image Classification

Effects of Training Parameters of AlexNet Architecture on Wound Image Classification

3D residual attention hierarchical fusion for real-time detection of the prostate capsule

3D residual attention hierarchical fusion for real-time detection of the prostate capsula

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