Orchard Boumans Algorithm and MRF Approach Based on Full Threshold Segmentation for Dental X-Ray Images

Rajpoot, Vikram; Dubey, Rahul; Khan, Safdar Sardar; Maheshwari, Saumil; Dixit, Abhishek; Deo, Arpit; Doohan, Nitika Vats

doi:10.18280/ts.390239

Cited by 3 publications

(4 citation statements)

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“…Rajpoot et al [27] proposed work in real time health care application uses the GMM and grabCut method.…”

Section: Related Workmentioning

confidence: 99%

Multiclass Adaptive Boosting Approach for Diabetic Retinopathy Prediction Using Diabetic Retinal Images

Shimpi¹,

Poonkuntran²

2023

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Scaling up diabetic retinopathy (DR) screening is crucial for preventing blindness caused by this prevalent eye condition, which affects an increasing number of individuals with diabetes worldwide. Early detection of DR and related complications through fundus imaging can effectively halt the progression of the disease to more severe stages. Although recent advancements in convolutional neural network (CNN) techniques have addressed some key challenges in DR screening, the issue of overfitting during the classification process remains due to the limited performance of CNNs in this context. In this study, we propose a novel multiclass adaptive boosting approach to overcome overfitting and enhance classification accuracy. We employ the VGG16 pretrained model for feature learning and the factor analysis method for preprocessing DR images. By integrating the adaptive boosting technique with CNN-based classification, our approach achieves significantly improved accuracy and area under the curve (AUC) scores. This research contributes to the development of more effective and efficient DR screening methods, with the potential to substantially impact diabetes management and patient outcomes.

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“…Rajpoot et al [27] proposed work in real time health care application uses the GMM and grabCut method.…”

Section: Related Workmentioning

confidence: 99%

Multiclass Adaptive Boosting Approach for Diabetic Retinopathy Prediction Using Diabetic Retinal Images

Shimpi¹,

Poonkuntran²

2023

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“…The input images fed to image preprocessing [28] before fed to prediction models. Marathi handwritten dataset handled for the ensemble learning experiment contains a total of 58 classes distributed as 120000 for training and 20000 for testing purposes.…”

Section: Dataset and Pre-processingmentioning

confidence: 99%

Enhancing Marathi Handwritten Character Recognition Using Ensemble Learning

Chikmurge¹,

Raghunathan²

2023

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In the current era, the Optical Character Recognition (OCR) model plays a vital role in converting images of handwritten characters or words into text editable script. During the COVID-19 pandemic, students' performance is assessed based on multiple-choice questions and handwritten answers so, in this situation, the need for handwritten recognition has become acute. Handwritten answers in any regional language need the OCR model to transform the readable machine-encoded text for automatic assessment which will reduce the burden of manual assessment. The single Convolutional Neural Network (CNN) algorithm recognizes the handwritten characters but its accuracy is suppressed when dataset volume is increased. In proposed work stacking and soft voting ensemble mechanisms that address multiple CNN models to recognize the handwritten characters. The performance of the ensemble mechanism is significantly better than the single CNN model. This proposed work ensemble VGG16, Alexnet and LeNet-5 as base classifiers using stacking and soft voting ensemble approaches. The overall accuracy of the proposed work is 98.66% when the soft voting ensemble has three CNN classifiers.

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“…Otsu's approach is a global thresholding method that Otsu proposed [31]. The appropriate threshold is set automatically and consistently using this method, which is based on the histogram's global attribute.…”

Section: Image Segmentation Based On Multithresholdingmentioning

confidence: 99%

“…Image after morphological operation 3.4.2 Image segmentation based on multithresholdingTo distinguish malignant objects from normal images, image segmentation is used. Multilevel thresholding utilizing the Otsu method is used for image segmentation where the image is divided into three levels using IM Quantize with two threshold levels.Otsu's approach is a global thresholding method that Otsu proposed[31]. The appropriate threshold is set automatically and consistently using this method, which is based on the histogram's global attribute.Let L be the total number of grey levels in the image.…”

mentioning

confidence: 99%

Morphological and Otsu’s Technique Based Mammography Mass Detection and Deep Neural Network Classifier Based Prediction

Chugh¹,

Goyal²,

Pandey³

et al. 2022

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Breast cancer is a leading cause of death among women. The death rate is reduced when this disease is detected early with the help of mammography. Deep learning is a method that radiologists use and request to help them make more accurate diagnoses and enhance their outcome predictions. This work presents a novel strategy comprising a pre-processing method and a mix of morphological and multi-thresholding using Otsu's technique based segmentation technique, which was tested on the Mini-MIAS dataset of 322 images. For Speed-Up Robust Features (SURF) selection, the inbuilt feature extraction is done utilizing multiple colour and texture features approaches. At the classification level, a new layer is added that performs 70 percent training and 30 percent testing of the deep neural network. There are two primary steps in the training phase: (1) Develop a model for dividing breast tissue into dense and non-dense categories. (2) Develop a model for classifying breast regions into mass and non-mass. The results show that the accuracy rate of the proposed automated DL approach is higher than that of other state-of-the-art models. The average accuracy (ACC) rates of the three types of cancer, i.e., normal, benign, and malignant cancer, utilizing the suggested method are 91 percent, 94 percent, and 93 percent, respectively, according to experimental results.

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Orchard Boumans Algorithm and MRF Approach Based on Full Threshold Segmentation for Dental X-Ray Images

Cited by 3 publications

References 0 publications

Multiclass Adaptive Boosting Approach for Diabetic Retinopathy Prediction Using Diabetic Retinal Images

Multiclass Adaptive Boosting Approach for Diabetic Retinopathy Prediction Using Diabetic Retinal Images

Enhancing Marathi Handwritten Character Recognition Using Ensemble Learning

Morphological and Otsu’s Technique Based Mammography Mass Detection and Deep Neural Network Classifier Based Prediction

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