ArticlePeerJ. Computer science2021
Histo-CADx: duo cascaded fusion stages for breast cancer diagnosis from histopathological images.
Article in PeerJ. Computer science, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
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Who cites it
21 citing papers in PubMed, 48 citations in OpenAlex.
- Harnessing infrared thermography and multi-convolutional neural networks for early breast cancer detection.Scientific reports · 2025Article
- Histopathological image based breast cancer diagnosis using deep learning and bio inspired optimization.Scientific reports · 2025Article
- Artificial intelligence in breast cancer diagnosis: A systematic literature review.Cambridge prisms. Precision medicine · 2025Review
- Skin cancer classification leveraging multi-directional compact convolutional neural network ensembles and gabor wavelets.Scientific reports · 2024Article
- Color-CADx: a deep learning approach for colorectal cancer classification through triple convolutional neural networks and discrete cosine transform.Scientific reports · 2024Article
- Deep Learning for Medical Image-Based Cancer Diagnosis.Cancers · 2023Review
- Classification of Breast Cancer Using Transfer Learning and Advanced Al-Biruni Earth Radius Optimization.Biomimetics (Basel, Switzerland) · 2023Article
- RADIC:A tool for diagnosing COVID-19 from chest CT and X-ray scans using deep learning and quad-radiomics.Chemometrics and intelligent laboratory systems : an international journal sponsored by the Chemometrics Society · 2023Article
- MobileNet-SVM: A Lightweight Deep Transfer Learning Model to Diagnose BCH Scans for IoMT-Based Imaging Sensors.Sensors (Basel, Switzerland) · 2023Article
- GabROP: Gabor Wavelets-Based CAD for Retinopathy of Prematurity Diagnosis via Convolutional Neural Networks.Diagnostics (Basel, Switzerland) · 2023Article
- Application of Deep Learning in Histopathology Images of Breast Cancer: A Review.Micromachines · 2022Review
- A Framework for Lung and Colon Cancer Diagnosis via Lightweight Deep Learning Models and Transformation Methods.Diagnostics (Basel, Switzerland) · 2022Article
- A wavelet-based deep learning pipeline for efficient COVID-19 diagnosis via CT slices.Applied soft computing · 2022Article
- Unsupervised Learning Based on Multiple Descriptors for WSIs Diagnosis.Diagnostics (Basel, Switzerland) · 2022Article
- An Intelligent ECG-Based Tool for Diagnosing COVID-19 via Ensemble Deep Learning Techniques.Biosensors · 2022Article
- ECG-BiCoNet: An ECG-based pipeline for COVID-19 diagnosis using Bi-Layers of deep features integration.Computers in biology and medicine · 2022Article
- Classification of Breast Cancer Histopathological Images Using DenseNet and Transfer Learning.Computational intelligence and neuroscience · 2022Article
- DIAROP: Automated Deep Learning-Based Diagnostic Tool for Retinopathy of Prematurity.Diagnostics (Basel, Switzerland) · 2021Article
- Intelligent Dermatologist Tool for Classifying Multiple Skin Cancer Subtypes by Incorporating Manifold Radiomics Features Categories.Contrast media & molecular imaging · 2021Article
- A computer-aided diagnostic framework for coronavirus diagnosis using texture-based radiomics images.Digital healthArticle
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Authors and funding
4 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Breast cancer (BC) is one of the most common types of cancer that affects females worldwide. It may lead to irreversible complications and even death due to late diagnosis and treatment. The pathological analysis is considered the gold standard for BC detection, but it is a challenging task. Automatic diagnosis of BC could reduce death rates, by creating a computer aided diagnosis (CADx) system capable of accurately identifying BC at an early stage and decreasing the time consumed by pathologists during examinations. This paper proposes a novel CADx system named Histo-CADx for the automatic diagnosis of BC. Most related studies were based on individual deep learning methods. Also, studies did not examine the influence of fusing features from multiple CNNs and handcrafted features. In addition, related studies did not investigate the best combination of fused features that influence the performance of the CADx. Therefore, Histo-CADx is based on two stages of fusion. The first fusion stage involves the investigation of the impact of fusing several deep learning (DL) techniques with handcrafted feature extraction methods using the auto-encoder DL method. This stage also examines and searches for a suitable set of fused features that could improve the performance of Histo-CADx. The second fusion stage constructs a multiple classifier system (MCS) for fusing outputs from three classifiers, to further improve the accuracy of the proposed Histo-CADx. The performance of Histo-CADx is evaluated using two public datasets; specifically, the BreakHis and the ICIAR 2018 datasets. The results from the analysis of both datasets verified that the two fusion stages of Histo-CADx successfully improved the accuracy of the CADx compared to CADx constructed with individual features. Furthermore, using the auto-encoder for the fusion process has reduced the computation cost of the system. Moreover, the results after the two fusion stages confirmed that Histo-CADx is reliable and has the capacity of classifying BC more accurately compared to other latest studies. Consequently, it can be used by pathologists to help them in the accurate diagnosis of BC. In addition, it can decrease the time and effort needed by medical experts during the examination.
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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 Socratic graph.