Hemorrhage Detection Based on 3D CNN Deep Learning Framework and Feature Fusion for Evaluating Retinal Abnormality in Diabetic Patients

Maqsood, Sarmad; Damaševičius, Robertas; Maskeliūnas, Rytis

doi:10.3390/s21113865

Cited by 49 publications

(24 citation statements)

References 52 publications

(57 reference statements)

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“…The following work is based on a deep learning approach. Maqsood et al [11] suggested a novel technique for identifying hemorrhage from retinal fundus images. The proposed method adopts a modified contrast enhancement method to improve the edge features of the input retinal fundus images.…”

Section: Literature Reviewmentioning

confidence: 99%

Hemorrhage Segmentation in Mobile-Phone Retinal Images Using Multi-Region Contrast Enhancement and Iterative Nick Thresholding Region Growing

Chandhakanond

Aimmanee

2022

Preprint

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Hemorrhages segmentation in retinal images is challenging because the sizes and shapes vary for each hemorrhage, the intensity is close to the blood vessels and macula, and the intensity within each hemorrhage is often non-uniform for the large ones. Hemorrhage segmentation in mobile-phone retinal images is even more challenging because mobile-phone retinal images usually have poorer contrast, more places of shadows, and uneven illumination compared to those obtained from the table-top ophthalmoscope. In this work, the proposed KMMRC-INRG method enhances the performance of the segmentation of the hemorrhages with the nonuniform intensity in poor lighting conditions on mobile-phone images. It improves the uneven illumination of mobile-phone retinal images using a proposed method, K-Mean Multi-Region Contrast Enhancement (KMMRC). It also enhances the segmentation of the boundary of the hemorrhage blobs using a novel Iterative Nick Thresholding Region Growing (INRG) method before applying an SVM classifier based on hue, saturation, and brightness features. We find this approach can achieve as high as 80.18%, 91.26%, and 85.36% for recall, precision, and F1-measure, respectively. The F1-measure score improves up to 19.02% compared to a state-of-the-art method DT-HSVE tested on the same full dataset and as much as 58.88% when considering only images with large-size hemorrhages.

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Section: Literature Reviewmentioning

confidence: 99%

Hemorrhage Segmentation in Mobile-Phone Retinal Images Using Multi-Region Contrast Enhancement and Iterative Nick Thresholding Region Growing

Chandhakanond

Aimmanee

2022

Preprint

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“…Recently, deep learning showed a huge improvement for cell segmentation [ 27 ], skin melanoma detection [ 28 ], hemorrhage detection [ 29 ], and a few more [ 30 , 31 ]. In medical imaging, deep learning was successful, especially for breast cancer [ 32 ], COVID-19 [ 33 ], Alzheimer’s disease recognition [ 34 ], brain tumor [ 35 ] diagnostics, and more [ 36 , 37 , 38 ].…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

Jabeen

Khan

Alhaisoni

et al. 2022

Sensors

Self Cite

125

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After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.

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“…The two main modules for detecting breast mass in the CAD system are detection of regions of interest (ROIs) and suspicious region identification based on segmentation and classification modules, which classify identified ROIs into benign or malignant categories [18,19]. A major phase that can strongly impact the classification rate is feature extraction [20,21]. Thus, this study aims to develop an efficient CAD system to detect breast cancer masses in mammogram images.…”

Section: Introductionmentioning

confidence: 99%

Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion

Zebari

Ibrahim²,

Zeebaree

et al. 2021

Applied Sciences

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Breast cancer detection using mammogram images at an early stage is an important step in disease diagnostics. We propose a new method for the classification of benign or malignant breast cancer from mammogram images. Hybrid thresholding and the machine learning method are used to derive the region of interest (ROI). The derived ROI is then separated into five different blocks. The wavelet transform is applied to suppress noise from each produced block based on BayesShrink soft thresholding by capturing high and low frequencies within different sub-bands. An improved fractal dimension (FD) approach, called multi-FD (M-FD), is proposed to extract multiple features from each denoised block. The number of features extracted is then reduced by a genetic algorithm. Five classifiers are trained and used with the artificial neural network (ANN) to classify the extracted features from each block. Lastly, the fusion process is performed on the results of five blocks to obtain the final decision. The proposed approach is tested and evaluated on four benchmark mammogram image datasets (MIAS, DDSM, INbreast, and BCDR). We present the results of single- and double-dataset evaluations. Only one dataset is used for training and testing in the single-dataset evaluation, whereas two datasets (one for training, and one for testing) are used in the double-dataset evaluation. The experiment results show that the proposed method yields better results on the INbreast dataset in the single-dataset evaluation, whilst better results are obtained on the remaining datasets in the double-dataset evaluation. The proposed approach outperforms other state-of-the-art models on the Mini-MIAS dataset.

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Hemorrhage Detection Based on 3D CNN Deep Learning Framework and Feature Fusion for Evaluating Retinal Abnormality in Diabetic Patients

Cited by 49 publications

References 52 publications

Hemorrhage Segmentation in Mobile-Phone Retinal Images Using Multi-Region Contrast Enhancement and Iterative Nick Thresholding Region Growing

Hemorrhage Segmentation in Mobile-Phone Retinal Images Using Multi-Region Contrast Enhancement and Iterative Nick Thresholding Region Growing

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion

Breast Cancer Detection Using Mammogram Images with Improved Multi-Fractal Dimension Approach and Feature Fusion

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