A Novel Transfer Learning-Based Model for Ultrasound Breast Cancer Image Classification

Gupta, Saksham; Agrawal, Satvik; Singh, Sunil K.; Kumar, Sudhakar

doi:10.1007/978-981-19-9819-5_37

Cited by 22 publications

(3 citation statements)

References 28 publications

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“…Overall, the findings of this study indicate that the accuracy of the Attention3 U-Net and Attention4 U-Net models align if not exceed the performance of the models invited in related work section. Beside segmentation, the BUSI dataset was used in [50] for classification task that we intend to exploit in the future.…”

Section: Table I Regroups the Training And Validation Results For Bothmentioning

confidence: 99%

Segmentation of Breast Cancer on Ultrasound Images using Attention U-Net Model

LAGHMATI,

HICHAM,

CHERRADI

et al. 2023

IJACSA

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Breast cancer (BC) is one of the most prevailing and life-threatening types of cancer impacting women worldwide. Early detection and accurate diagnosis are crucial for effective treatment and improved patient outcomes. Deep learning techniques have shown remarkable promise in medical image analysis tasks, particularly segmentation. This research leverages the Breast Ultrasound Images BUSI dataset to develop two variations of a segmentation model using the Attention U-Net architecture. In this study, we trained the Attention3 U-Net and the Attention4 U-net on the BUSI dataset, consisting of normal, benign, and malignant breast lesions. We evaluated the model's performance based on standard segmentation metrics such as the Dice coefficient and Intersection over Union (IoU). The results demonstrate the effectiveness of the Attention U-Net in accurately segmenting breast lesions, with high overall performance, indicating agreement between predicted and ground truth masks. The successful application of the Attention U-Net to the BUSI dataset holds promise for improving breast cancer diagnosis and treatment. It highlights the potential of deep learning in medical image analysis, paving the way for more efficient and reliable diagnostic tools in breast cancer management.

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Section: Table I Regroups the Training And Validation Results For Bothmentioning

confidence: 99%

Segmentation of Breast Cancer on Ultrasound Images using Attention U-Net Model

LAGHMATI,

HICHAM,

CHERRADI

et al. 2023

IJACSA

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show abstract

“…Similarly, in robotics for Industry 4.0, the models fortify fault tolerance, addressing challenges and advancing resilient automation in smart manufacturing [25]. The Stochastic Byzantine Fault Tolerance Models, designed for P2P networks, seamlessly extend to healthcare with a transfer learning-based model for image classification of diseases like cancer [24].…”

Section: Introductionmentioning

confidence: 99%

Adaptive Stochastic Fault Tolerance for Self-Healing P2P Networks

Kumar,

Goyal,

Singh

et al. 2024

Preprint

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In the complex domain of reliable and flexible distributed systems, the challenging issue of Byzantine fault resilience is pervasive, particularly within the intricate framework of Peer-to-Peer (P2P) networks. The 'Adaptive Stochastic Fault Tolerance for Self-Healing P2P Networks' proposes a novel approach to self-healing in distributed peer-to-peer (P2P) systems. It combines ideas from stochastic Byzantine fault tolerance models with adaptive resilience strategies to detect and recover from failures within these complex networks by using probabilistic modeling. This allows P2P systems to adjust their resilience measures in real-time, effectively shielding themselves from unforeseen faults. Furthermore, the research provides extensive experimentation and empirical validation to demonstrate the effectiveness of the proposed approach.

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“…Developing different prediction algorithms for critical infrastructure system (I. ) (Peñalvo, Maan, et al, 2022) (S. Gupta et al, 2023) with sustainable development (Chopra et al, 2022;Peñalvo, Sharma, et al, 2022) (Bouncken et al, 2022;M. Singh et al, 2023) is an art which involves a deep understanding of the underlying systems, the potential risks they face, and a creative approach to designing algorithms with minimum overheads (S. Kumar et al, 2021) (S. Kumar et al, 2022) (P. S. Kumar, 2022;.…”

Section: Introductionmentioning

confidence: 99%

A Novel Deep Federated Learning-Based Model to Enhance Privacy in Critical Infrastructure Systems

Sharma,

Singh,

Chhabra

et al. 2023

International Journal of Software Science and Computational Intelligence

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Deep learning (DL) can provide critical infrastructure operators with valuable insights and predictive capabilities to help them make more informed decisions, improving system's robustness. However, training DL models requires large amounts of data, which can be costly to store in a centralized manner. Storing large amounts of sensitive critical infrastructure data in the cloud can pose significant security risks. Federated learning (FL) allows several clients to share learning data and train ML models. Unlike centralized models, FL does not require the sharing of client data. A novel framework is presented to train a VGG16 based CNN global model without sharing the data and only updating the local models among clients using federated averaging. For experimentation, MNIST dataset is used. The framework achieves high accuracy and keep data private using FL in critical infrastructures. The benefits and challenges of FL along with security vulnerabilities and attacks have been discussed along with the defenses that can be used to mitigate these attacks.

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A Novel Transfer Learning-Based Model for Ultrasound Breast Cancer Image Classification

Cited by 22 publications

References 28 publications

Segmentation of Breast Cancer on Ultrasound Images using Attention U-Net Model

Segmentation of Breast Cancer on Ultrasound Images using Attention U-Net Model

Adaptive Stochastic Fault Tolerance for Self-Healing P2P Networks

A Novel Deep Federated Learning-Based Model to Enhance Privacy in Critical Infrastructure Systems

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