Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning

Aljuaid, Hanan; Alturki, Nazik; Alsubaie, Najah; Cavallaro, Lucia; Liotta, Antonio

doi:10.1016/j.cmpb.2022.106951

Cited by 129 publications

(58 citation statements)

References 25 publications

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“…Lung Cancer Data Set (LCDS) [ 18 ]. This dataset was chosen as it contains information on patients who had surgeries.…”

Section: Methodsmentioning

confidence: 99%

“…An interesting proposal based on bio-inspired algorithms is put forward by González-Patiño et al [ 15 ], yielding promising results for breast cancer classification. Recently, deep learning has been analyzed, and has been reported as a useful tool for this task [ 16 , 17 , 18 ]. In addition, there has been an increase over the past year in the use of bio-inspired techniques for automatic breast cancer detection [ 19 , 20 , 21 ].…”

Section: Related Workmentioning

confidence: 99%

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A Novel Bioinspired Algorithm for Mixed and Incomplete Breast Cancer Data Classification

González-Patiño

Villuendas-Rey

Saldaña-Pérez

et al. 2023

IJERPH

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The pre-diagnosis of cancer has been approached from various perspectives, so it is imperative to continue improving classification algorithms to achieve early diagnosis of the disease and improve patient survival. In the medical field, there are data that, for various reasons, are lost. There are also datasets that mix numerical and categorical values. Very few algorithms classify datasets with such characteristics. Therefore, this study proposes the modification of an existing algorithm for the classification of cancer. The said algorithm showed excellent results compared with classical classification algorithms. The AISAC-MMD (Mixed and Missing Data) is based on the AISAC and was modified to work with datasets with missing and mixed values. It showed significantly better performance than bio-inspired or classical classification algorithms. Statistical analysis established that the AISAC-MMD significantly outperformed the Nearest Neighbor, C4.5, Naïve Bayes, ALVOT, Naïve Associative Classifier, AIRS1, Immunos1, and CLONALG algorithms in conducting breast cancer classification.

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“…Lung Cancer Data Set (LCDS) [ 18 ]. This dataset was chosen as it contains information on patients who had surgeries.…”

Section: Methodsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

A Novel Bioinspired Algorithm for Mixed and Incomplete Breast Cancer Data Classification

González-Patiño

Villuendas-Rey

Saldaña-Pérez

et al. 2023

IJERPH

View full text Add to dashboard Cite

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“…Transfer learning [137] aims to enhance the performance of downstream models by transferring information from different (but related) source domains. In order to use knowledge representation of feature maps to untrained cancer datasets, Kakati et al [138] employed transfer learning to a CNN model called DEGnext, to predict the significant up-regulating (UR) and down-regulating (DR) genes from gene expression data received from The Cancer Genome Atlas database.…”

Section: Transfer Learningmentioning

confidence: 99%

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Alharbi

Vakanski

2023

Bioengineering

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Cancer is a term that denotes a group of diseases caused by the abnormal growth of cells that can spread in different parts of the body. According to the World Health Organization (WHO), cancer is the second major cause of death after cardiovascular diseases. Gene expression can play a fundamental role in the early detection of cancer, as it is indicative of the biochemical processes in tissue and cells, as well as the genetic characteristics of an organism. Deoxyribonucleic acid (DNA) microarrays and ribonucleic acid (RNA)-sequencing methods for gene expression data allow quantifying the expression levels of genes and produce valuable data for computational analysis. This study reviews recent progress in gene expression analysis for cancer classification using machine learning methods. Both conventional and deep learning-based approaches are reviewed, with an emphasis on the application of deep learning models due to their comparative advantages for identifying gene patterns that are distinctive for various types of cancers. Relevant works that employ the most commonly used deep neural network architectures are covered, including multi-layer perceptrons, as well as convolutional, recurrent, graph, and transformer networks. This survey also presents an overview of the data collection methods for gene expression analysis and lists important datasets that are commonly used for supervised machine learning for this task. Furthermore, we review pertinent techniques for feature engineering and data preprocessing that are typically used to handle the high dimensionality of gene expression data, caused by a large number of genes present in data samples. The paper concludes with a discussion of future research directions for machine learning-based gene expression analysis for cancer classification.

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“…The variational approaches that make use of the non-local self-similarity feature of natural images have inspired the general architecture of the proposed network. Using this idea as a foundation, we develop deep networks capable of non-local processing that also greatly benefit from discriminative training [ 18 ]. Due to the complex nature of data collection, developing an unsupervised speckle-reduction solution for practical purposes is a difficult challenge.…”

Section: Literature Reviewmentioning

confidence: 99%

Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique

Vimala

Srinivasan

Mathivanan

et al. 2023

Sensors

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Rapid improvements in ultrasound imaging technology have made it much more useful for screening and diagnosing breast problems. Local-speckle-noise destruction in ultrasound breast images may impair image quality and impact observation and diagnosis. It is crucial to remove localized noise from images. In the article, we have used the hybrid deep learning technique to remove local speckle noise from breast ultrasound images. The contrast of ultrasound breast images was first improved using logarithmic and exponential transforms, and then guided filter algorithms were used to enhance the details of the glandular ultrasound breast images. In order to finish the pre-processing of ultrasound breast images and enhance image clarity, spatial high-pass filtering algorithms were used to remove the extreme sharpening. In order to remove local speckle noise without sacrificing the image edges, edge-sensitive terms were eventually added to the Logical-Pool Recurrent Neural Network (LPRNN). The mean square error and false recognition rate both fell below 1.1% at the hundredth training iteration, showing that the LPRNN had been properly trained. Ultrasound images that have had local speckle noise destroyed had signal-to-noise ratios (SNRs) greater than 65 dB, peak SNR ratios larger than 70 dB, edge preservation index values greater than the experimental threshold of 0.48, and quick destruction times. The time required to destroy local speckle noise is low, edge information is preserved, and image features are brought into sharp focus.

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Computer-aided diagnosis for breast cancer classification using deep neural networks and transfer learning

Cited by 129 publications

References 25 publications

A Novel Bioinspired Algorithm for Mixed and Incomplete Breast Cancer Data Classification

A Novel Bioinspired Algorithm for Mixed and Incomplete Breast Cancer Data Classification

Machine Learning Methods for Cancer Classification Using Gene Expression Data: A Review

Image Noise Removal in Ultrasound Breast Images Based on Hybrid Deep Learning Technique

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