Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study

AlShammari, Maha M.; Almuhanna, Afnan F; Alhiyafi, Jamal

doi:10.3390/s22010203

Cited by 21 publications

(12 citation statements)

References 48 publications

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“…They ( 20 ) used machine learning-based algorithms to help the radiologist read mammography pictures and classify the tumor in an acceptable amount of time in this study. They extracted a number of features from the mammogram's region of interest, which the physician manually labeled.…”

Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Detection and Classification Empowered With Transfer Learning

Arooj

Atta-ur-Rahman

Zubair

et al. 2022

Front. Public Health

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Cancer is a major public health issue in the modern world. Breast cancer is a type of cancer that starts in the breast and spreads to other parts of the body. One of the most common types of cancer that kill women is breast cancer. When cells become uncontrollably large, cancer develops. There are various types of breast cancer. The proposed model discussed benign and malignant breast cancer. In computer-aided diagnosis systems, the identification and classification of breast cancer using histopathology and ultrasound images are critical steps. Investigators have demonstrated the ability to automate the initial level identification and classification of the tumor throughout the last few decades. Breast cancer can be detected early, allowing patients to obtain proper therapy and thereby increase their chances of survival. Deep learning (DL), machine learning (ML), and transfer learning (TL) techniques are used to solve many medical issues. There are several scientific studies in the previous literature on the categorization and identification of cancer tumors using various types of models but with some limitations. However, research is hampered by the lack of a dataset. The proposed methodology is created to help with the automatic identification and diagnosis of breast cancer. Our main contribution is that the proposed model used the transfer learning technique on three datasets, A, B, C, and A2, A2 is the dataset A with two classes. In this study, ultrasound images and histopathology images are used. The model used in this work is a customized CNN-AlexNet, which was trained according to the requirements of the datasets. This is also one of the contributions of this work. The results have shown that the proposed system empowered with transfer learning achieved the highest accuracy than the existing models on datasets A, B, C, and A2.

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Section: Literature Reviewmentioning

confidence: 99%

Breast Cancer Detection and Classification Empowered With Transfer Learning

Arooj

Atta-ur-Rahman

Zubair

et al. 2022

Front. Public Health

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“…Many researchers used various strategies to reach promising findings. The authors in [28] employed computer vision techniques to help a radiologist evaluate mammography images and diagnose a malignancy in an acceptable amount of time. They extracted various characteristics from the mammogram's selected area, which the surgeon subjectively labeled.…”

Section: Related Workmentioning

confidence: 99%

A Novel CNN-Inception-V4-Based Hybrid Approach for Classification of Breast Cancer in Mammogram Images

Nazir

Khan

Mohiyuddin

et al. 2022

Wireless Communications and Mobile Computing

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Breast cancer is the most frequent disease in women, with one in every 19 women at risk. Breast cancer is the fifth leading cause of cancer death in women around the world. The most effective and efficient technique of controlling cancer development is early identification. Mammography helps in the early detection of cancer, which saves lives. Many studies conducted various tests to categorize the tumor and obtained positive findings. However, there are certain limits. Mass categorization in mammography is still a problem, although it is critical in aiding radiologists in establishing correct diagnoses. The purpose of this study is to develop a unique hybrid technique to identify breast cancer mass pictures as benign or malignant. The combination of two networks helps accelerate the categorization process. This study proposes a novel-based hybrid approach, CNN-Inception-V4, based on the fusing of these two networks. Mass images are used in this research from the CBIS-DDSM dataset. 450 images are taken for benign, and 450 images are used for malignant. The images are first cleaned by removing pectoral muscles, labels, and white borders. Then, CLAHE is used to these images to improve their quality in order to produce promising classification results. Following preprocessing, our model classifies cancer in mammography pictures as benign or malignant abnormalities. Our proposed model’s accuracy is 99.2%, with sensitivity of 99.8%, specificity of 96.3%, and F1-score of 97%. We also compared our proposed model to CNN, Inception-V4, and ResNet-50. Our proposed model outperforms existing classification models, according to the results.

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“…For assessing the hunting behaviour of the AVSC method has been considered unimodal functions which involve a single optima value. Tabulated results of optimizers on these functions have been reported by tables (5)(6)(7)(8)(9)(10). Attained solutions reveal that the AVSC method is able to trap the best optima value in the complex domain with least number of iterations than others.…”

Section: Hunting Skillmentioning

confidence: 99%

“…Maha M. Alshammari et. al [8] was able to achieve 100% accuracy during classifying the tumor into malignant and benign. The experimentation is done on a new database taken from King Fahd Hospital of the University (KFUH), Saudi Arabia.…”

Section: Related Workmentioning

confidence: 99%

A Newly Hybrid Version of African Vultures Optimization Algorithm and Sine Cosine Algorithm for Mammographic Masses and its Diagnosis

Kaur

Cheema

2022

Preprint

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From the last few periods, breast cancer is the foremost reason for cancer-related huge death rates in the midst of females. However, the recent study detection and diagnosis of these types of issues can successfully decrease the death rate. Masses are unique indicators of undetectable breast cancer noticeable in mammograms whose identification and judgment is a more complex or stimulating job due to its restrained natural surroundings. The seriousness of this issue is to motivate the scientists of different areas for their solution. With the great inspiration of this learning, in this work we are tried to present a new hybrid algorithm with the integration powerful features of two different algorithms such as African Vultures Optimization Algorithm (AVOA) and Sine Cosine Optimization Algorithm (SCA) and it is called AVSC method for identify the breast cancer. The features of the sine cosine algorithm helps to improve the exploitation and exploration phases of the basic AVOA method. This enhancement has been designed to improve the premature convergence, avoiding the local optima and fast convergence speed and many more. To verify the performance of the proposed scheme have been verified 29-CEC ’2017 test functions. Moreover, the AVSC method has been successfully applied on the two different mini-MIAS and DDSM databases including 68 and 500 mammograms, etc and attained a highest rate of sensitivity and false positives per image for the respective datasets. Hence, the tabulated results give the strong evidence that the AVSC is able to effectively resolve the highly complex tasks that others whole could be more helpful in biomedical sciences.

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Mammography Image-Based Diagnosis of Breast Cancer Using Machine Learning: A Pilot Study

Cited by 21 publications

References 48 publications

Breast Cancer Detection and Classification Empowered With Transfer Learning

Breast Cancer Detection and Classification Empowered With Transfer Learning

A Novel CNN-Inception-V4-Based Hybrid Approach for Classification of Breast Cancer in Mammogram Images

A Newly Hybrid Version of African Vultures Optimization Algorithm and Sine Cosine Algorithm for Mammographic Masses and its Diagnosis

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