ArticleSensors (Basel, Switzerland)2022
ABCanDroid: A Cloud Integrated Android App for Noninvasive Early Breast Cancer Detection Using Transfer Learning.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers.
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Who cites it
9 citing papers in PubMed.
- BraNet: a mobil application for breast image classification based on deep learning algorithms.Medical & biological engineering & computing · 2024Article
- Emerging Sensor Communication Network-Based AI/ML Driven Intelligent IoT.Sensors (Basel, Switzerland) · 2023Article
- Analyzing Histological Images Using Hybrid Techniques for Early Detection of Multi-Class Breast Cancer Based on Fusion Features of CNN and Handcrafted.Diagnostics (Basel, Switzerland) · 2023Article
- MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors.Sensors (Basel, Switzerland) · 2023Article
- On the Analyses of Medical Images Using Traditional Machine Learning Techniques and Convolutional Neural Networks.Archives of computational methods in engineering : state of the art reviews · 2023Article
- Improved Bald Eagle Search Optimization with Synergic Deep Learning-Based Classification on Breast Cancer Imaging.Cancers · 2022Article
- ResNet-32 and FastAI for diagnoses of ductal carcinoma from 2D tissue slides.Scientific reports · 2022Article
- Trusted Data Storage Architecture for National Infrastructure.Sensors (Basel, Switzerland) · 2022Article
- Classification of breast cancer using a manta-ray foraging optimized transfer learning framework.PeerJ. Computer science · 2022Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Many patients affected by breast cancer die every year because of improper diagnosis and treatment. In recent years, applications of deep learning algorithms in the field of breast cancer detection have proved to be quite efficient. However, the application of such techniques has a lot of scope for improvement. Major works have been done in this field, however it can be made more efficient by the use of transfer learning to get impressive results. In the proposed approach, Convolutional Neural Network (CNN) is complemented with Transfer Learning for increasing the efficiency and accuracy of early detection of breast cancer for better diagnosis. The thought process involved using a pre-trained model, which already had some weights assigned rather than building the complete model from scratch. This paper mainly focuses on ResNet101 based Transfer Learning Model paired with the ImageNet dataset. The proposed framework provided us with an accuracy of 99.58%. Extensive experiments and tuning of hyperparameters have been performed to acquire the best possible results in terms of classification. The proposed frameworks aims to be an efficient tool for all doctors and society as a whole and help the user in early detection of breast cancer.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.