65P Nottingham grade; does it influence the survival of operable breast cancer patients across all TNM stages?

Peiris, Harshini; Mudduwa, Lakmini; Thalagala, Neil; Jayatilake, K; Ekanayake, U.; Horadugoda, J

doi:10.1093/annonc/mdv519.14

Cited by 2 publications

(1 citation statement)

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“…Medical image analysis is one of the most exciting research areas that have gained significant attention in academia and the medical sector, allowing for the detection and treatment of various illnesses, including breast cancer ( Dewangan et al, 2022 ; Zerouaoui & Idri, 2022 ; Chowdhury et al, 2022 ; Mohamed et al, 2022 ). Breast cancers are classified into five stages according to Peiris et al (2015) . The stages progress from pre-cancerous to metastatic or advanced cancer.…”

Section: Introductionmentioning

confidence: 99%

Classification of breast cancer using a manta-ray foraging optimized transfer learning framework

Baghdadi

Malki

Balaha

et al. 2022

PeerJ Computer Science

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Due to its high prevalence and wide dissemination, breast cancer is a particularly dangerous disease. Breast cancer survival chances can be improved by early detection and diagnosis. For medical image analyzers, diagnosing is tough, time-consuming, routine, and repetitive. Medical image analysis could be a useful method for detecting such a disease. Recently, artificial intelligence technology has been utilized to help radiologists identify breast cancer more rapidly and reliably. Convolutional neural networks, among other technologies, are promising medical image recognition and classification tools. This study proposes a framework for automatic and reliable breast cancer classification based on histological and ultrasound data. The system is built on CNN and employs transfer learning technology and metaheuristic optimization. The Manta Ray Foraging Optimization (MRFO) approach is deployed to improve the framework’s adaptability. Using the Breast Cancer Dataset (two classes) and the Breast Ultrasound Dataset (three-classes), eight modern pre-trained CNN architectures are examined to apply the transfer learning technique. The framework uses MRFO to improve the performance of CNN architectures by optimizing their hyperparameters. Extensive experiments have recorded performance parameters, including accuracy, AUC, precision, F1-score, sensitivity, dice, recall, IoU, and cosine similarity. The proposed framework scored 97.73% on histopathological data and 99.01% on ultrasound data in terms of accuracy. The experimental results show that the proposed framework is superior to other state-of-the-art approaches in the literature review.

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