Optimal breast tumor diagnosis using discrete wavelet transform and deep belief network based on improved sunﬂower optimization method

Shen, Lizhen; He, Mingyi; Shen, Ning; Yousefi, Nasser; Wang, Chao; Liu, Guoqing

doi:10.1016/j.bspc.2020.101953

Cited by 29 publications

(9 citation statements)

References 32 publications

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“…A few deep-learningbased methods for classifying breast cancer masses in mammography pictures have been proposed. In [9], authors created a deep-belief-network (DBN)-based approach for determining whether mammography images are normal or abnormal. A discrete wavelet transform was used to extract image features, specifically the gray level co-occurrence matrix features from the HL and LL wavelet sub-bands.…”

Section: Related Workmentioning

confidence: 99%

A CNN Hyperparameters Optimization Based on Particle Swarm Optimization for Mammography Breast Cancer Classification

Aguerchi,

Jabrane,

Habba

et al. 2024

J. Imaging

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Breast cancer is considered one of the most-common types of cancers among females in the world, with a high mortality rate. Medical imaging is still one of the most-reliable tools to detect breast cancer. Unfortunately, manual image detection takes much time. This paper proposes a new deep learning method based on Convolutional Neural Networks (CNNs). Convolutional Neural Networks are widely used for image classification. However, the determination process for accurate hyperparameters and architectures is still a challenging task. In this work, a highly accurate CNN model to detect breast cancer by mammography was developed. The proposed method is based on the Particle Swarm Optimization (PSO) algorithm in order to look for suitable hyperparameters and the architecture for the CNN model. The CNN model using PSO achieved success rates of 98.23% and 97.98% on the DDSM and MIAS datasets, respectively. The experimental results proved that the proposed CNN model gave the best accuracy values in comparison with other studies in the field. As a result, CNN models for mammography classification can now be created automatically. The proposed method can be considered as a powerful technique for breast cancer prediction.

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Section: Related Workmentioning

confidence: 99%

A CNN Hyperparameters Optimization Based on Particle Swarm Optimization for Mammography Breast Cancer Classification

Aguerchi,

Jabrane,

Habba

et al. 2024

J. Imaging

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“…For the early diagnosis of BC, Shen et al introduced a CAD system 38 . To extract features, GLCM is combined with discrete wavelet decomposition (DWD), and Deep Belief Network (DBN) is utilized for classification.…”

Section: Related Workmentioning

confidence: 99%

Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance

Elkorany

Elsharkawy

2023

Sci Rep

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Breast cancer (BC) is spreading more and more every day. Therefore, a patient's life can be saved by its early discovery. Mammography is frequently used to diagnose BC. The classification of mammography region of interest (ROI) patches (i.e., normal, malignant, or benign) is the most crucial phase in this process since it helps medical professionals to identify BC. In this paper, a hybrid technique that carries out a quick and precise classification that is appropriate for the BC diagnosis system is proposed and tested. Three different Deep Learning (DL) Convolution Neural Network (CNN) models—namely, Inception-V3, ResNet50, and AlexNet—are used in the current study as feature extractors. To extract useful features from each CNN model, our suggested method uses the Term Variance (TV) feature selection algorithm. The TV-selected features from each CNN model are combined and a further selection is performed to obtain the most useful features which are sent later to the multiclass support vector machine (MSVM) classifier. The Mammographic Image Analysis Society (MIAS) image database was used to test the effectiveness of the suggested method for classification. The mammogram's ROI is retrieved, and image patches are assigned to it. Based on the results of testing several TV feature subsets, the 600-feature subset with the highest classification performance was discovered. Higher classification accuracy (CA) is attained when compared to previously published work. The average CA for 70% of training is 97.81%, for 80% of training, it is 98%, and for 90% of training, it reaches its optimal value. Finally, the ablation analysis is performed to emphasize the role of the proposed network’s key parameters.

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“…They found that the Relief-F algorithm outperformed other feature selection algorithms, and the best result among the classifiers was given by K-NN. Shen et al [10] proposed a CAD system for mammogram image classification into two classes: normal and abnormal. They enhanced mammography images using noise reduction and that further used image segmentation and morphological operations.…”

Section: Hand-engineering Approachmentioning

confidence: 99%

“…Much research has been conducted on this problem, and some studies have yielded good results [3][4][5][6][7]. Previous studies have either used hand-engineered feature representation [8][9][10] or a deep learning approach [3][4][5][6][7]. For the hand-crafted approach, the main limitations are its time-consuming nature and its need for an expert to determine what the suitable feature for a certain problem is [11].…”

Section: Introductionmentioning

confidence: 99%

An Efficient Method for Breast Mass Classification Using Pre-Trained Deep Convolutional Networks

et al. 2022

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Masses are the early indicators of breast cancer, and distinguishing between benign and malignant masses is a challenging problem. Many machine learning- and deep learning-based methods have been proposed to distinguish benign masses from malignant ones on mammograms. However, their performance is not satisfactory. Though deep learning has been shown to be effective in a variety of applications, it is challenging to apply it for mass classification since it requires a large dataset for training and the number of available annotated mammograms is limited. A common approach to overcome this issue is to employ a pre-trained model and fine-tune it on mammograms. Though this works well, it still involves fine-tuning a huge number of learnable parameters with a small number of annotated mammograms. To tackle the small set problem in the training or fine-tuning of CNN models, we introduce a new method, which uses a pre-trained CNN without any modifications as an end-to-end model for mass classification, without fine-tuning the learnable parameters. The training phase only identifies the neurons in the classification layer, which yield higher activation for each class, and later on uses the activation of these neurons to classify an unknown mass ROI. We evaluated the proposed approach using different CNN models on the public domain benchmark datasets, such as DDSM and INbreast. The results show that it outperforms the state-of-the-art deep learning-based methods.

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Optimal breast tumor diagnosis using discrete wavelet transform and deep belief network based on improved sunﬂower optimization method

Cited by 29 publications

References 32 publications

A CNN Hyperparameters Optimization Based on Particle Swarm Optimization for Mammography Breast Cancer Classification

A CNN Hyperparameters Optimization Based on Particle Swarm Optimization for Mammography Breast Cancer Classification

Efficient breast cancer mammograms diagnosis using three deep neural networks and term variance

An Efficient Method for Breast Mass Classification Using Pre-Trained Deep Convolutional Networks

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