Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network

Mohanakurup, Vinodkumar; Gangadharan, Syam Machinathu Parambil; Goel, Pallavi; Verma, Devvret; Alshehri, Sameer; Kashyap, Ramgopal; Malakhil, Baitullah

doi:10.1155/2022/8517706

Cited by 196 publications

(3 citation statements)

References 28 publications

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“…To address this problem, a unique framework is developed. Combining CNNs with traditional numerical methods is what this method [22] tries to do to make showing complicated thermal dynamics more accurate, useful, and flexible. Table 3 displays the simulation parameters for the four techniques in this dataset.…”

Section: Proposed Methodologymentioning

confidence: 99%

Enhancing unsteady heat transfer simulation in porous media through the application of convolutional neural networks

Saraireh

2024

Eng. Res. Express

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This research describes a novel technique for anticipating unstable heat transfer in porous media. Convolutional neural networks (CNNs) are used with finite volume method (FVM) and long short-term memory (LSTM) networks to accomplish this. Heat transport networks are difficult to characterise using traditional numerical methodologies owing to their nonlinearity and complexity. The proposed solution combines FVM's precise physical modelling with CNN's and LSTM's superior pattern identification and temporal analysis. This collaboration supports the suggested strategy. Heat transport dynamics simulations in porous materials are more accurate, efficient, and adaptable when employing this hybrid framework. The experimental setup focused on porous material properties and gathered and processed a large amount of data. The building's three-dimensional shape, heat transfer, and time were investigated. Temporal fluctuations were also used. Multiple indicators are used to evaluate the overall performance of the model. These criteria include convergence speed, F1 score, accuracy, precision, recall, and computational cost. In the most notable numerical results, the proposed strategy surpasses both the Finite Element and the Lattice Boltzmann methods. The presented method enabled fast convergence and reduced processing costs. These results were: accuracy (0.92), precision (0.93), recall (0.91), and F1 score (0.92). The proposed method is generalizable and adaptable, and it can address a variety of heat transport simulation problems in porous media. Unlike CNNs, which can identify significant spatial patterns, LSTM cells can only see temporal dynamics. These two components are required to show heat transfer, which is a continually changing phenomenon. Modern technology enables more complex simulations. Processing expenses are lowered, and estimations are more accurate. These two discoveries were obtained through the inquiry and methodologies. Finally, the CNN-FVM-LSTM technique simulates heat transport using complicated computer models. Predicting unusually high temperatures in porous materials may improve the model's accuracy, computational efficiency, and flexibility.

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Section: Proposed Methodologymentioning

confidence: 99%

Enhancing unsteady heat transfer simulation in porous media through the application of convolutional neural networks

Saraireh

2024

Eng. Res. Express

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“…VGG-16 is made up of 16 convolutional layers as compared with AlexNet's 5 convolutional layers and 3 fully connected layers. VGG-16 uses the highest filter size of 4×, while AlexNet utilizes the smallest filter size of 3 × 3 [34].…”

Section: Feature Extractionmentioning

confidence: 99%

Hybrid Facial Emotion Recognition Using CNN-Based Features

et al. 2023

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In computer vision, the convolutional neural network (CNN) is a very popular model used for emotion recognition. It has been successfully applied to detect various objects in digital images with remarkable accuracy. In this paper, we extracted learned features from a pre-trained CNN and evaluated different machine learning (ML) algorithms to perform classification. Our research looks at the impact of replacing the standard SoftMax classifier with other ML algorithms by applying them to the FC6, FC7, and FC8 layers of Deep Convolutional Neural Networks (DCNNs). Experiments were conducted on two well-known CNN architectures, AlexNet and VGG-16, using a dataset of masked facial expressions (MLF-W-FER dataset). The results of our experiments demonstrate that Support Vector Machine (SVM) and Ensemble classifiers outperform the SoftMax classifier on both AlexNet and VGG-16 architectures. These algorithms were able to achieve improved accuracy of between 7% and 9% on each layer, suggesting that replacing the classifier in each layer of a DCNN with SVM or ensemble classifiers can be an efficient method for enhancing image classification performance. Overall, our research demonstrates the potential for combining the strengths of CNNs and other machine learning (ML) algorithms to achieve better results in emotion recognition tasks. By extracting learned features from pre-trained CNNs and applying a variety of classifiers, we provide a framework for investigating alternative methods to improve the accuracy of image classification.

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“…It also needs topic expertise as well as the support of a radiologist. Deep learning (DL) models, on the other hand, may extract features from an input dataset and generate an adaptive learning process depending on the goal output [14]. The DL techniques significantly accelerate data processing and pattern extraction activities, enabling for the reuse of approaches Various studies have been conducted to look at breast cancer images from various angles [15].…”

Section: Introductionmentioning

confidence: 99%

Image Classification for Breast Cancer Using a Modified Convolution Neural Network Architecture

Porwal,

Singh,

2023

IJRITCC

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The most common type of cancer that results in death is breast cancer. In the world, millions of people struggle with this disease. Breast cancer can affect men and women but women are more affected. For awareness, it is necessary to understand the sign and symptoms of breast cancer. The most common sign is an abnormal lump in the breast. But there may be many reasons of develop abnormal lumps. Computer-Aided Diagnosis (CAD) is extensively used in pathological image analysis to help pathologists enhance diagnosis efficiency, accuracy, and consistency. Recent studies have looked into deep learning methodologies to improve the effectiveness of pathological CAD.

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Breast Cancer Detection on Histopathological Images Using a Composite Dilated Backbone Network

Cited by 196 publications

References 28 publications

Enhancing unsteady heat transfer simulation in porous media through the application of convolutional neural networks

Enhancing unsteady heat transfer simulation in porous media through the application of convolutional neural networks

Hybrid Facial Emotion Recognition Using CNN-Based Features

Image Classification for Breast Cancer Using a Modified Convolution Neural Network Architecture

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