Diabetic retinopathy detection and classification using hybrid feature set

Amin, Javeria; Sharif, Muhammad; Rehman, Amjad; Raza, Mudassar; Mufti, Muhammad Rafiq

doi:10.1002/jemt.23063

Cited by 41 publications

(17 citation statements)

References 42 publications

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“…Preprocessing is normally performed in almost all sorts of image analysis and classification to remove the unnecessary detail and enhance the input image quality for better performance (Adeel et al, 2020; Al‐Ameen et al, 2015; Amin, Sharif, Raza, Saba, & Anjum, 2019; Amin, Sharif, Raza, Saba, & Rehman, 2019; Amin, Sharif, Raza, Saba, Sial, & Shad, 2019; Amin, Sharif, Rehman, Raza, & Mufti, 2018; Amin, Sharif, Yasmin, Saba, & Raza, 2019; Ejaz et al, 2018; Ejaz et al, 2020; Iqbal et al, 2019; Iqbal, Ghani, Saba, & Rehman, 2018; Iqbal, Khan, Saba, & Rehman, 2017; Javed, Rahim, & Saba, 2019; Javed, Rahim, Saba, & Rashid, 2019; Javed, Rahim, Saba, & Rehman, 2020; Javed, Saba, Shafry, & Rahim, 2020; Saba, 2020; Saba, Bokhari, et al, 2018). Accordingly, in the proposed approach, image intensities are normalized using histogram equalization (Ali et al, 2016) and Gaussian un‐sharp mask is applied to enhanced its quality.…”

Section: Proposed Methodologymentioning

confidence: 99%

Light microscopic iris classification using ensemble multi‐class support vector machine

Rehman

2021

Microscopy Res & Technique

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Similar to other biometric systems such as fingerprint, face, DNA, iris classification could assist law enforcement agencies in identifying humans. Iris classification technology helps law‐enforcement agencies to recognize humans by matching their iris with iris data sets. However, iris classification is challenging in the real environment due to its invertible and complex texture variations in the human iris. Accordingly, this article presents an improved Oriented FAST and Rotated BRIEF with Bag‐of‐Words model to extract distinct and robust features from the iris image, followed by ensemble multi‐class‐SVM to classify iris. The proposed methodology consists of four main steps; first, iris image normalization and enhancement; second, localizing iris region; third, iris feature extraction; finally, iris classification using ensemble multi‐class support vector machine. For preprocessing of input images, histogram equalization, Gaussian mask and median filters are applied. The proposed technique is tested on two benchmark databases, that is, CASIA‐v1 and iris image database, and achieved higher accuracy than other existing techniques reported in state of the art.

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Section: Proposed Methodologymentioning

confidence: 99%

Light microscopic iris classification using ensemble multi‐class support vector machine

Rehman

2021

Microscopy Res & Technique

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“…Given the shortage of trained radiologists and the huge numbers of affected patients, automatic abnormality identification would assist early diagnosis. Automation is possible using Artificial Intelligence (AI) and other potentially powerful Machine Learning (ML) methods [11][12][13]. Here, we take a unique approach toward early COVID19 infection detection.…”

Section: Sars-cov-2 (Also Known As Covid-19mentioning

confidence: 99%

Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images

Amin¹,

Sharif²,

Anjum³

et al. 2021

Computers, Materials &Amp; Continua

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can cause severe pneumonia that may be fatal. Correct diagnosis is essential. Computed tomography (CT) usefully detects symptoms of COVID-19 infection. In this retrospective study, we present an improved framework for detection of COVID-19 infection on CT images; the steps include pre-processing, segmentation, feature extraction/fusion/selection, and classification. In the pre-processing phase, a Gabor wavelet filter is applied to enhance image intensities. A marker-based, watershed controlled approach with thresholding is used to isolate the lung region. In the segmentation phase, COVID-19 lesions are segmented using an encoder-/decoder-based deep learning model in which deepLabv3 serves as the bottleneck and mobilenetv2 as the classification head. DeepLabv3 is an effective decoder that helps to refine segmentation of lesion boundaries. The model was trained using fine-tuned hyperparameters selected after extensive experimentation. Subsequently, the Gray Level Co-occurrence Matrix (GLCM) features and statistical features including circularity, area, and perimeters were computed for each segmented image. The computed features were serially fused and the best features (those that were optimally discriminatory) selected using a Genetic Algorithm (GA) for classification. The performance of the method was evaluated using two benchmark datasets: The COVID-19 Segmentation and the POF Hospital datasets. The results were better than those of existing methods.

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“…Brain tumors are a deadly illness and are blamed for several deaths worldwide per year (Amin, Sharif, et al, 2020; Amin, Sharif, Raza, Saba, & Anjum, 2019; Amin, Sharif, Raza, Saba, & Rehman, 2019; Amin, Sharif, Rehman, Raza, & Mufti, 2018; Amin, Sharif, Yasmin, Saba, & Raza, 2019). Brain tumor appears in two kinds: benign and malignant.…”

Section: Introductionmentioning

confidence: 99%

Brain tumor detection and multi‐classification using advanced deep learning techniques

Sadad

Rehman

Munir

et al. 2021

Microscopy Res & Technique

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156

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A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, and therapies of brain tumors. Therefore, manual brain tumor detection is complicated, time-consuming, and vulnerable to error. Hence, automated computer-assisted diagnosis at high precision is currently in demand. This article presents segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU). The preprocessing and data augmentation concept were introduced to enhance the classification rate. The multi-classification of brain tumors is performed using evolutionary algorithms and reinforcement learning through transfer learning. Other deep learning methods such as ResNet50, DenseNet201, MobileNet V2, and InceptionV3 are also applied. Results thus obtained exhibited that the proposed research framework performed better than reported in state of the art. Different CNN, models applied for tumor classification such as MobileNet V2, Inception V3, ResNet50, Den-seNet201, NASNet and attained accuracy 91.8, 92.8, 92.9, 93.1, 99.6%, respectively. However, NASNet exhibited the highest accuracy.Two processes of transfer learning: freeze and fine-tune, are performed to extract significant features from MRI slices. Brain tumor multi-classification is performed using transfer learning, ResNet50-UNet, and NASNet architecture.

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Diabetic retinopathy detection and classification using hybrid feature set

Cited by 41 publications

References 42 publications

Light microscopic iris classification using ensemble multi‐class support vector machine

Light microscopic iris classification using ensemble multi‐class support vector machine

Diagnosis of COVID-19 Infection Using Three-Dimensional Semantic Segmentation and Classification of Computed Tomography Images

Brain tumor detection and multi‐classification using advanced deep learning techniques

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