Preprocessing Breast Cancer Dataset from Ultrasound Screening to Improve Data Quality for Diagnosis Modelling

Eltalhi, Saria; Kutrani, Huda; Azzuz, Raouf; Elbadri, Fathia A. A.

doi:10.1109/mi-sta54861.2022.9837553

Cited by 3 publications

(3 citation statements)

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“…The preprocessing step aims to improve the quality of the medical image by applying Figure 1: Major Steps of a conventional CADx system various techniques such as noise removal, geometric transformation, cropping, resizing, and adjusting the color balance of the images. 24,25 The preprocessed image proceeds to the segmentation step. The segmentation step aims to identify and isolate areas of interest within the image.…”

Section: Conventional Cadx Systemsmentioning

confidence: 99%

Machine Learning Approaches for Skin Neoplasm Diagnosis

Asaduzzaman,

Thompson,

Uddin

2024

Preprint

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Approaches for skin neoplasm diagnosis include physical exam, skin biopsy, lab tests of biopsy samples, and image analyses. These approaches often involve error-prone and time-consuming processes. Recent studies show that machine learning has promises to effectively classify skin images into different classes such as melanoma and melanocytic nevi. In this work, we investigate machine learning approaches to enhance the performance of computer-aided diagnosis (CADx) systems to diagnose skin diseases. In the proposed CADx system, generative adversarial network (GAN) is used to identify (and remove) fake images. Exploratory data analysis (EDA) is applied to normalize the original dataset for preventing model overfitting. Synthetic minority over-sampling technique (SMOTE) is employed to rectify class imbalances in the original dataset. To accurately classify skin images, the following four machine learning models are utilized: linear discriminant analysis (LDA), support vector machine (SVM), convolutional neural network (CNN), and an ensemble CNN-SVM. Experimental results using the HAM10000 dataset demonstrate the ability of the machine learning models to improve CADx performance in treating skin neoplasm. Initially, the LDA, SVM, CNN, and ensemble CNN-SVM show 49%, 72%, 77%, and 79% accuracy, respectively. After applying GAN, EDA, and SMOTE, the LDA, SVM, CNN, and ensemble CNN-SVM show 76%, 83%, 87%, and 94% accuracy, respectively. We plan to explore other machine learning models and datasets in our next endeavor.

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Section: Conventional Cadx Systemsmentioning

confidence: 99%

Machine Learning Approaches for Skin Neoplasm Diagnosis

Asaduzzaman,

Thompson,

Uddin

2024

Preprint

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show abstract

“…The preprocessing step aims to improve the quality of the medical image by applying various techniques such as noise removal, geometric transformation, cropping, resizing, and adjusting the color balance of the images. , The preprocessed image proceeds to the segmentation step. The segmentation step aims to identify and isolate areas of interest within the image. − Figure shows an example of the implementation of noise removal from the preprocessing stage and thresholding used in segmentation.…”

Section: Related Workmentioning

confidence: 99%

Machine Learning Approaches for Skin Neoplasm Diagnosis

Asaduzzaman,

Thompson,

Uddin

2024

ACS Omega

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Approaches for skin neoplasm diagnosis include physical exam, skin biopsy, lab tests of biopsy samples, and image analyses. These approaches often involve error-prone and time-consuming processes. Recent studies show that machine learning shows promise in effectively classifying skin images into different categories such as melanoma and melanocytic nevi. In this work, we investigate machine learning approaches to enhance the performance of computer-aided diagnosis (CADx) systems to diagnose skin diseases. In the proposed CADx system, generative adversarial network (GAN) discriminator is used to identify (and remove) fake images. Exploratory data analysis (EDA) is applied to normalize the original data set for preventing model overfitting. Synthetic minority oversampling technique (SMOTE) is employed to rectify class imbalances in the original data set. To accurately classify skin images, the following machine learning models are utilized: linear discriminant analysis (LDA), support vector machine (SVM), convolutional neural network (CNN), and an ensemble CNN−SVM. Experimental results using the HAM10000 data set demonstrate the ability of the machine learning models to improve CADx performance in treating skin neoplasm. Initially, the LDA, SVM, CNN, and ensemble CNN−SVM show 49%, 72%, 77%, and 79% accuracy, respectively. After applying GAN (discriminator) and SMOTE, the LDA, SVM, CNN, and ensemble CNN−SVM show 76%, 83%, 87%, and 94% accuracy, respectively. We plan to explore other machine learning models and data sets in our next endeavor.

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“…8,9 Recently, data mining methods have been extensively applied in BC diagnosis and treatment. 10 They play a crucial role in reducing the frequency of false positives and false-negative results, aiding physicians in decisionmaking, and assisting researchers in identifying disease patterns and predicting outcomes when dealing with numerous variables. 11 Recent studies analyzing medical survival data have shown that the decision tree (DT; c5) exhibits the highest accuracy, surpassing 90% when compared to artificial neural networks (ANNs) and logistic regression methods.…”

Section: Introductionmentioning

confidence: 99%

Survivability Prediction of Breast Cancer Patients Using Three Data Mining Methods: A Comparative Study

Jalali,

Ghasemi,

Nematolahi

et al. 2024

Epidemiol Health System J

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Background and aims: Breast cancer (BC) is the leading cause of mortality among women. Early diagnosis is crucial for effective treatment. This study applied suitable data mining methods that provide rules and present influential prognostic factors on the survival time of BC patients. Methods: The dataset consisted of 1574 women diagnosed between January 2002 and December 2012 at the Cancer Registry Center of Nemazi hospital in Fars Province, Iran. Patients were classified based on prognostic factors using three popular data mining methods, including decision tree (J48), Naïve Bayes (NB), and nominal logistic regression (NLR). The Weka software was considered to compare these methods using sensitivity, specificity, and accuracy metrics. The outcome of the study was the median survival time, which was categorized into three classes. Results: In total, 212 women (13.5%) died of BC, whose mean age was 49.74 years old. Overall survival rates at 2, 3, 5, and 10 years were 0.98, 0.94, 0.87, and 0.76, respectively. The mean and median survival times were 4.81 and 4.27 years. Sensitivity, specificity, and accuracy for J48 and NB were 0.480, 0.570, and 0.572, as well as 0.483, 0.610, and 0.584, respectively. In addition, the corresponding values were 0.488, 0.584, and 0.579 for NLR, respectively. Further, J48 showed that the Nottingham Prognostic Index (NPI) was the most influential prognostic factor. Conclusion: This paper sought to improve the accuracy of BC classification using data mining methods. Comparing multiple prediction models gave us an insight into the relative prediction abilities of different data mining methods. The results suggested NB as the best classifier due to its higher accuracy and specificity. Finally, J48 identified the NPI as the most effective prognostic factor.

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Preprocessing Breast Cancer Dataset from Ultrasound Screening to Improve Data Quality for Diagnosis Modelling

Cited by 3 publications

References 0 publications

Machine Learning Approaches for Skin Neoplasm Diagnosis

Machine Learning Approaches for Skin Neoplasm Diagnosis

Machine Learning Approaches for Skin Neoplasm Diagnosis

Survivability Prediction of Breast Cancer Patients Using Three Data Mining Methods: A Comparative Study

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