A Novel Approach Based on Decreased Dimension and Reduced Gray Level Range Matrix Features for Stone Texture Classification

Murthy, G. S. N.; Rao., Srininvasa; Veerraju, T.

doi:10.11591/ijece.v7i5.pp2502-2513

Cited by 6 publications

(5 citation statements)

References 12 publications

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“…The most important is it has been one of the best programming languages for perform mathematical algorithms. It consists of matrix operations, plotting of functions and data, operation of algorithms, user interfaces, and other interfacing [21]. Matlab also have several device boxes that valuable for signal processing, image processing, optimization and others [22].…”

Section: Methodsmentioning

confidence: 99%

JPG, PNG and BMP image compression using discrete cosine transform

et al. 2021

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This paper proposes image compression using discrete cosine transform (DCT) for the format of joint photographic expert groups (JPEG) or JPG, portable network graphic (PNG) and bitmap (BMP). These three extensions are the most popular types used in current image processing storage. The purpose of image compression is to produce lower memory usage or to reduce memory file. This process removes redundant information of each pixel. The challenge for image compression process is to maintain the quality of images after the compression process. Hence, this article utilizes the DCT technique to sustain the image quality and at the same time reduces the image storage size. The effectiveness of the DCT technique has been reasonable over some real images and the implementation of the technique has been compared with different types of image extensions. Matlab software is an important platform for this project in order to write a program and perform the progress of project phase by phase to achieve the expected results. Based on the tested images, the DCT technique in image compression is capable to reduce the image storage memory in average about 50% of each image tested.

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Section: Methodsmentioning

confidence: 99%

JPG, PNG and BMP image compression using discrete cosine transform

et al. 2021

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“…Besides this, in [11], a new technique is built for reducing the stone's image dimension along with its gray level range without losing any substantial information. In [12], the authors developed a "significance-weighted principal component analysis" technique for reducing deviations in intensity and boosting the statistical power to detect group differences.…”

Section: International Journal On Recent and Innovationmentioning

confidence: 99%

Augmented MRI Images for Classification of Normal and Tumors Brain through Transfer Learning Techniques

Angurala

2023

IJRITCC

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A brain tumor is a severe malignant condition caused by uncontrolled and abnormal cell division. Recent advances in deep learning have aided the health business in Medical Imaging for the diagnosis of numerous disorders. The most frequent and widely used deep learning algorithm for visual learning and image recognition. This research seeks to multi-classification tumors in the brain from images attained by Magnetic Resonance Imaging (MRI) using deep learning models that have been pre-trained for transfer learning. As per the publicly available MRI brain tumor dataset, brain tumors identified as glioma, meningioma, and pituitary, are accounting for most brain tumors. To ensure the robustness of the suggested method, data acquisition, and preprocessing are performed in the first step followed by data augmentation. Finally, Transfer Learning algorithms including DenseNet, ResNetV2, and InceptionResNetv2 have been applied to find out the optimum algorithm based on various parameters including accuracy, precision, and recall, and are under the curve (AUC). The experimental outcomes show that the model’s validation accuracy is high for DenseNet (about 97%), while ResNetv2 and InceptionResNetv2 achieved 77% and 80% only.

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“…The principal component analysis (PCA) is one of the effective procedures which utilized for picture acknowledgment and pressure. The reason for PCA is to diminish the substantial dimensionality of the information recovered from the sensor [13], [14] In Probabilistic neural network (PNN) classifier helps for Tumor classification which produced good results than the adaboost classifier. However there are problems in Back propagation (BP) Learning.…”

Section: A Feature Extraction Using Gray Level Covariance Matrix (Glcmentioning

confidence: 99%

Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensor

et al. 2019

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Now-a-days image processing placed an important role for recognizing various diseases such as breast, lung, and brain tumors in earlier stage for giving the appropriate treatment. Presently, most cancer diagnosis worked according to the visual examination process with effectively. Human visual reviewing of infinitesimal biopsy pictures is exceptionally tedious, subjective, and conflicting due to between and intra-onlooker varieties. In this manner, the malignancy and it's compose will be distinguished in a beginning time for finish treatment and fix. This brain tumor classification system using machine learningbased back propagation neural networks (MLBPNN) causes pathologists to enhance the exactness and proficiency in location of threat and to limit the entomb onlooker variety. Moreover, the technique may assist doctors with analyzing the picture cell by utilizing order and bunching calculations by recoloring qualities of the phones. The different picture preparing steps required for disease location from biopsy pictures incorporate procurement, upgrade, and division; include extraction, picture portrayal, characterization, and basic leadership. In this paper, MLBPNN is analyzed with the help of infra-red sensor imaging technology. Then, the computational multifaceted nature of neural distinguishing proof incredibly diminished when the entire framework is deteriorated into a few subsystems. The features are extracted using fractal dimension algorithm and then the most significant features are selected using multi fractal detection technique to reduce the complexity. This imaging sensor is integrated via wireless infrared imaging sensor which is produced to transmit the tumor warm data to a specialist clinician to screen the wellbeing condition and for helpful control of ultrasound measurements level, especially if there should arise an occurrence of elderly patients living in remote zones.INDEX TERMS Wireless infrared imaging sensor, infra-red sensor, principal component analysis gray level covariance matrix, machine learning based neural networks.

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A Novel Approach Based on Decreased Dimension and Reduced Gray Level Range Matrix Features for Stone Texture Classification

Cited by 6 publications

References 12 publications

JPG, PNG and BMP image compression using discrete cosine transform

JPG, PNG and BMP image compression using discrete cosine transform

Augmented MRI Images for Classification of Normal and Tumors Brain through Transfer Learning Techniques

Neural Network Based Brain Tumor Detection Using Wireless Infrared Imaging Sensor

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