ArticleSensors (Basel, Switzerland)2020
Spectral-Spatial Features Integrated Convolution Neural Network for Breast Cancer Classification.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 46 citations in OpenAlex.
- Frequency-domain multi-scale hybrid attention for pathological image classification.Scientific reports · 2026Article
- BC-SwinNet: Swin transformer and CNN with multi-objective optimization for multi-class breast cancer detection using Histopathological images.Scientific reports · 2026Article
- Deep learning-based quantitative histopathology of endoscopic biopsies in Crohn's disease: a retrospective cross-sectional validation study.Frontiers in immunology · 2026Article
- Equilibrium Optimization-Based Ensemble CNN Framework for Breast Cancer Multiclass Classification Using Histopathological Image.Diagnostics (Basel, Switzerland) · 2024Article
- 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
- A Survey of Convolutional Neural Network in Breast Cancer.Computer modeling in engineering & sciences : CMES · 2023Article
- Article
- Multi-Classification of Breast Cancer Lesions in Histopathological Images Using DEEP_Pachi: Multiple Self-Attention Head.Diagnostics (Basel, Switzerland) · 2022Article
- A novel wavelet decomposition and transformation convolutional neural network with data augmentation for breast cancer detection using digital mammogram.Scientific reports · 2022Article
- Semi-supervised vision transformer with adaptive token sampling for breast cancer classification.Frontiers in pharmacology · 2022Article
- Breast cancer histopathological images classification based on deep semantic features and gray level co-occurrence matrix.PloS one · 2022Article
- Meta-Heuristic Algorithm-Tuned Neural Network for Breast Cancer Diagnosis Using Ultrasound Images.Frontiers in oncology · 2022Article
- Ensemble deep learning for brain tumor detection.Frontiers in computational neuroscience · 2022Article
- Deep Learning on Histopathology Images for Breast Cancer Classification: A Bibliometric Analysis.Healthcare (Basel, Switzerland) · 2021Article
- Breast histopathological image analysis using image processing techniques for diagnostic puposes: A methodological review.Journal of medical systems · 2021Review
- Computer Vision-Based Microcalcification Detection in Digital Mammograms Using Fully Connected Depthwise Separable Convolutional Neural Network.Sensors (Basel, Switzerland) · 2021Article
- Exploiting deep neural network and long short-term memory method-ologies in bioacoustic classification of LPC-based features.PloS one · 2021Article
Corrections and comments
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Authors and funding
5 authors at 4 institutions in 3 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Cancer identification and classification from histopathological images of the breast depends greatly on experts, and computer-aided diagnosis can play an important role in disagreement of experts. This automatic process has increased the accuracy of the classification at a reduced cost. The advancement in Convolution Neural Network (CNN) structure has outperformed the traditional approaches in biomedical imaging applications. One of the limiting factors of CNN is it uses spatial image features only for classification. The spectral features from the transform domain have equivalent importance in the complex image classification algorithm. This paper proposes a new CNN structure to classify the histopathological cancer images based on integrating the spectral features obtained using a multi-resolution wavelet transform with the spatial features of CNN. In addition, batch normalization process is used after every layer in the convolution network to improve the poor convergence problem of CNN and the deep layers of CNN are trained with spectral-spatial features. The proposed structure is tested on malignant histology images of the breast for both binary and multi-class classification of tissue using the BreaKHis Dataset and the Breast Cancer Classification Challenge 2015 Datasest. Experimental results show that the combination of spectral-spatial features improves classification accuracy of the CNN network and requires less training parameters in comparison with the well known models (i.e., VGG16 and ALEXNET). The proposed structure achieves an average accuracy of 97.58% and 97.45% with 7.6 million training parameters on both datasets, respectively.
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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.