Performance Measurement of Various Hybridized Kernels for Noise Normalization and Enhancement in High-Resolution MR Images

Srinivasu, Parvathaneni Naga; Bălaş, Valentina Emilia; Norwawi, Norita Md

doi:10.1007/978-981-15-5495-7_1

Cited by 19 publications

(3 citation statements)

References 26 publications

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“…(1) www.nature.com/scientificreports/ Because of its intrinsic efficiency and robustness, the LBP technique is often used to construct texture features enabling the classification of image pixels 33 . The adaptive threshold generates the Binary Vector 34,35 . The threshold T is asses as shown in Eq.…”

Section: Local Binary Pattern a Local Binary Pattern (Lbpmentioning

confidence: 99%

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

Praveen

Srinivasu

Shafi

et al. 2022

Sci Rep

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Carcinoma is a primary source of morbidity in women globally, with metastatic disease accounting for most deaths. Its early discovery and diagnosis may significantly increase the odds of survival. Breast cancer imaging is critical for early identification, clinical staging, management choices, and treatment planning. In the current study, the FastAI technology is used with the ResNet-32 model to precisely identify ductal carcinoma. ResNet-32 is having few layers comparted to majority of its counterparts with almost identical performance. FastAI offers a rapid approximation toward the outcome for deep learning models via GPU acceleration and a faster callback mechanism, which would result in faster execution of the model with lesser code and yield better precision in classifying the tissue slides. Residual Network (ResNet) is proven to handle the vanishing gradient and effective feature learning better. Integration of two computationally efficient technologies has yielded a precision accuracy with reasonable computational efforts. The proposed model has shown considerable efficiency in the evaluating parameters like sensitivity, specificity, accuracy, and F1 Score against the other dominantly used deep learning models. These insights have shown that the proposed approach might assist practitioners in analyzing Breast Cancer (BC) cases appropriately, perhaps saving future complications and death. Clinical and pathological analysis and predictive accuracy have been improved with digital image processing.

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Section: Local Binary Pattern a Local Binary Pattern (Lbpmentioning

confidence: 99%

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

Praveen

Srinivasu

Shafi

et al. 2022

Sci Rep

View full text Add to dashboard Cite

show abstract

“…The MRI images are being segmented using fully automated mechanisms such as thresholding [ 19 ], region growing [ 20 ], and edge-based segmentation [ 21 – 23 ]. The threshold-based segmentation mechanism is suitable for MRI images, and the studies have proven that experimenting with diffusion-weighted MRI images has obtained 86% accuracy [ 24 ].…”

Section: Literature Reviewmentioning

confidence: 99%

Variational Autoencoders‐BasedSelf‐Learning Model for Tumor Identification and Impact Analysis from 2‐D MRI Images

Srinivasu

Krishna²,

Ahmed

et al. 2023

Journal of Healthcare Engineering

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Over the past few years, a tremendous change has occurred in computer-aided diagnosis (CAD) technology. The evolution of numerous medical imaging techniques has enhanced the accuracy of the preliminary analysis of several diseases. Magnetic resonance imaging (MRI) is a prevalent technology extensively used in evaluating the progress of the spread of malignant tissues or abnormalities in the human body. This article aims to automate a computationally efficient mechanism that can accurately identify the tumor from MRI images and can analyze the impact of the tumor. The proposed model is robust enough to classify the tumors with minimal training data. The generative variational autoencoder models are efficient in reconstructing the images identical to the original images, which are used in adequately training the model. The proposed self-learning algorithm can learn from the insights from the autogenerated images and the original images. Incorporating long short-term memory (LSTM) is faster processing of the high dimensional imaging data, making the radiologist’s task and the practitioners more comfortable assessing the tumor’s progress. Self-learning models need comparatively less data for the training, and the models are more resource efficient than the various state-of-art models. The efficiency of the proposed model has been assessed using various benchmark metrics, and the obtained results have exhibited an accuracy of 89.7%. The analysis of the progress of tumor growth is presented in the current study. The obtained accuracy is not pleasing in the healthcare domain, yet the model is reasonably fair in dealing with a smaller size dataset by making use of an image generation mechanism. The study would outline the role of an autoencoder in self-learning models. Future technologies may include sturdy feature engineering models and optimized activation functions that would yield a better result.

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“…Tang et al [39] introduce a semantic-aware image fusion network (SeAFusion), which leverages the semantic segmentation task to guide the image fusion with a gradient residual dense block (GRDB). More multi-modal image fusion techniques in medical imaging are discussed in Tirupal et al [40] and Srinvasu et al [41].…”

Section: B Image Fusionmentioning

confidence: 99%

Guided Image Deblurring by Deep Multi-Modal Image Fusion

2022

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Estimating sharp images from blurry observations is still a difficult task in the image processing research field. Previous works may produce deblurred images that lose details or contain artifacts. To deal with this problem, a feasible solution is to seek the help of additional images, such as the nearinfrared image and the flashlight image, etc. In this paper, we propose a fusion framework for image deblurring, called Guided Deblurring Fusion Network (GDFNet), to integrate the multi-modal information for better image deblurring performance. Unlike previous works that directly compute a deblurred image using paired multi-modal degraded and guidance images, GDFNet employs image fusion techniques to obtain a deblurred image. GDFNet can combine the advantages by fusing the pre-deblurred streams of single and guided image deblurring using convolutional neural network (CNN). We adopt a blur/residual image splitting strategy by fusing the residual images to enhance the representation ability of encoders and preserve details. We employ a 2-level coarse-to-fine reconstruction strategy to improve the fusion and deblurring performance by supervising its multi-scale output. Quantitative comparisons on multi-modal image datasets show that our GDFNet can recover correct structures and produce fewer artifacts while preserving more details. The peak signal-to-noise ratio (PSNR) of GDFNet (with MPR) evaluated on the blurry/flash dataset is at least 0.9 dB higher than the compared algorithms.INDEX TERMS Blind image deblurring, guided image deblurring, image deblurring, image fusion, multimodal image fusion.

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Performance Measurement of Various Hybridized Kernels for Noise Normalization and Enhancement in High-Resolution MR Images

Cited by 19 publications

References 26 publications

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides

Variational Autoencoders‐BasedSelf‐Learning Model for Tumor Identification and Impact Analysis from 2‐D MRI Images

Guided Image Deblurring by Deep Multi-Modal Image Fusion

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