A CNN-based methodology for breast cancer diagnosis using thermal images
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TLDR
It is demonstrated that a CAD system that implements data-augmentation techniques reach identical performance metrics in comparison with a system that uses a bigger database but without data-AUgmentation, and the influence of data pre-processing, data augmentation and database size on several CAD models is studied.Abstract:
A recent study from GLOBOCAN disclosed that during 2018 two million women worldwide had been diagnosed with breast cancer. Currently, mammography, magnetic resonance imaging, ultrasound, and biopsi...read more
Citations
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Journal ArticleDOI
IoMT Cloud-Based Intelligent Prediction of Breast Cancer Stages Empowered With Deep Learning
Shahan Yamin Siddiqui,Amir Haider,Taher M. Ghazal,Muhammad Adnan Khan,Iftikhar Naseer,Sagheer Abbas,MuhibUr Rahman,Junaid Ahmad Khan,Munir Ahmad,Mohammad Kamrul Hasan,Afifi Mohammed. A,Karamath Ateeq +11 more
TL;DR: Wang et al. as mentioned in this paper proposed an Internet of Medical Things (IoMT) cloud-based model for the intelligent prediction of breast cancer stages, which is employed to detect breast cancer and its stages.
Journal ArticleDOI
Automatic Detection of White Blood Cancer From Bone Marrow Microscopic Images Using Convolutional Neural Networks
Deepika Kumar,Nikita Jain,Aayush Khurana,Sweta Mittal,Suresh Chandra Satapathy,Roman Senkerik,Jude Hemanth +6 more
TL;DR: This study indicates that the DCNN model’s performance is close to that of the established CNN architectures with far fewer parameters and computation time tested on the retrieved dataset, Thus, the model can be used effectively as a tool for determining the type of cancer in the bone marrow.
Journal ArticleDOI
Thermal Imaging - An Emerging Modality for Breast Cancer Detection: A Comprehensive Review
Aayesha Hakim,R. N. Awale +1 more
TL;DR: Thermography is a promising research problem and a potential solution for early detection of breast cancer in younger women, and supplementary research is needed to affirm the potential of this technology for predicting breast cancer risk effectively.
Journal ArticleDOI
Detecting Vasodilation as Potential Diagnostic Biomarker in Breast Cancer Using Deep Learning-Driven Thermomics.
TL;DR: By non-invasively capturing a thermal map of the entire tumor, the proposed method can assist in screening and diagnosing this malignancy and may preoperatively stratify the patients for personalized treatment planning and potentially monitor the patients during treatment.
References
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