Discrete Wavelet Transform-Based CNN for Breast Cancer Classification from Histopathology Images

Qi, Yuan; Liu, Xiaozhang; Li, Hua; Liu, Mingting; Li, Wei

doi:10.1007/978-3-031-20096-0_25

Cited by 2 publications

(1 citation statement)

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“…The model joined two DCNNs to derive differentiated image aspects by employing TL. Authors in [18] introduced a frequency domain learning technique that relies on CNN and DWT for the classification task of BC HPIs. In [19], an enhanced model, IBESSDL-BCHI, was introduced for BC recognition by employing HPIs.…”

Section: Related Workmentioning

confidence: 99%

Breast Cancer Classification from Histopathological Images using Future Search Optimization Algorithm and Deep Learning

Gurumoorthy,

Kamarasan

2024

Eng. Technol. Appl. Sci. Res.

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In medical imaging, precise recognition of Breast Cancer (BC) is a challenge due to the complications of breast tissues. Histopathological detection is still considered the standard in BC detection. Still, the dramatic increase in workload and the complexity of histopathological image (HPI) make this task labor-intensive and dependent on the pathologist, making the advance of automated and precise HPI analysis techniques needed. Due to the automated feature extraction capability, Deep Learning (DL) methods have been effectively used in different sectors, particularly in the medical imaging sector. This study develops the future search algorithm with a DL-based breast cancer detection and classification (FSADL-BCDC) method. The FSADL-BCDC technique examines HPIs to detect and classify BC. To achieve this, the FSADL-BCDC technique implements Wiener Filtering (WF)-based preprocessing to eliminate the noise in the images. Additionally, the FSADL-BCDC uses the ResNeXt method for feature extraction with a Future Search Algorithm (FSA)-based tuning procedure. For BCDC, the FSADL-BCDC technique employs a Hybrid Convolutional Neural Network along with the Long Short-Term Memory (HCNN-LSTM) approach. Finally, the Sunflower Optimization (SFO) approach adjusts the hyperparameter values of the HCNN-LSTM. The outcomes of the FSADL-BCDC are inspected on a standard medical image dataset. Extensive relational studies highlighted the improved performance of the FSADL-BCDC approach in comparison with known methods by exhibiting an output of 96.94% and 98.69% under diverse datasets.

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Section: Related Workmentioning

confidence: 99%