Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques

Aamir, Sanam; Rahim, Aqsa; Aamir, Zain; Abbasi, Saadullah Farooq; Khan, Muhammad Shahbaz; Alhaisoni, Majed; Khan, Muhammad Attique; Khan, Khyber; Ahmad, Jawad

doi:10.1155/2022/5869529

Cited by 22 publications

(17 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…[12] Similarly, Aamir et other models reviewed. [13] In another paper, models were created using machine learning techniques to detect and visualize important prognostic indicators of breast cancer survival rate. In terms of both model accuracy and calibration measurement, all algorithms produced close results from the lowest decision tree (accuracy = 79.8) and highest random forest (accuracy = 82.7).…”

Section: Figure 2 Correlation Matric Of Variablementioning

confidence: 99%

Breast Cancer Risk Prediction with Stochastic Gradient Boosting

Kivrak¹

2022

Clin Cancer Investig J

View full text Add to dashboard Cite

Breast cancer, which is an important public health problem worldwide, is one of the deadliest cancers in women. This study aims to classify open-access breast cancer data and identify important risk factors with the Stochastic Gradient Boosting Method. The open-access breast cancer dataset was used to construct a classification model in the study. Stochastic Gradient Boosting was used to classify the disease. Balanced accuracy, accuracy, sensitivity, specificity, and positive/negative predictive values were evaluated for model performance. The accuracy, balanced accuracy, sensitivity, specificity, positive predictive value, negative predictive value, and F1 score metrics obtained with the Stochastic Gradient Boosting model were 100 %, 100 %, 100 %, 100 %, 100 %, and 100 %, and 100 % respectively. In addition, the importance of the variables obtained, the most important risk factors for breast cancer were a cave. points_mean, area_worst, and perimeter_worst, concave. points_worst respectively. According to the study results, with the machine-learning model Stochastic Gradient Boosting used, patients with and without breast cancer were classified with high accuracy, and the importance of the variables related to cancer status was determined. Factors with high variable importance can be considered potential risk factors associated with cancer status and can play an essential role in disease diagnosis.

show abstract

Section: Figure 2 Correlation Matric Of Variablementioning

confidence: 99%

Breast Cancer Risk Prediction with Stochastic Gradient Boosting

Kivrak¹

2022

Clin Cancer Investig J

View full text Add to dashboard Cite

show abstract

“…Over the past two decades, extensive research has been conducted to develop effective approaches for achieving precise segmentation [13][14][15][16]. Deep learning (DL) models, predominantly convolutional neural networks (CNNs), have proven remarkably successful in accurately segmenting anatomical structures and identifying pathological regions in various medical imaging modalities, including X-ray, MRI, CT, and ultrasound [17][18][19][20][21][22][23][24][25][26].…”

Section: Introductionmentioning

confidence: 99%

Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet

Khaledyan,

Marini,

M. Baran

et al. 2023

PLoS ONE

View full text Add to dashboard Cite

Segmentation of breast ultrasound images is a crucial and challenging task in computer-aided diagnosis systems. Accurately segmenting masses in benign and malignant cases and identifying regions with no mass is a primary objective in breast ultrasound image segmentation. Deep learning (DL) has emerged as a powerful tool in medical image segmentation, revolutionizing how medical professionals analyze and interpret complex imaging data. The UNet architecture is a highly regarded and widely used DL model in medical image segmentation. Its distinctive architectural design and exceptional performance have made it popular among researchers. With the increase in data and model complexity, optimization and fine-tuning models play a vital and more challenging role than before. This paper presents a comparative study evaluating the effect of image preprocessing and different optimization techniques and the importance of fine-tuning different UNet segmentation models for breast ultrasound images. Optimization and fine-tuning techniques have been applied to enhance the performance of UNet, Sharp UNet, and Attention UNet. Building upon this progress, we designed a novel approach by combining Sharp UNet and Attention UNet, known as Sharp Attention UNet. Our analysis yielded the following quantitative evaluation metrics for the Sharp Attention UNet: the Dice coefficient, specificity, sensitivity, and F1 score values obtained were 0.93, 0.99, 0.94, and 0.94, respectively. In addition, McNemar’s statistical test was applied to assess significant differences between the approaches. Across a number of measures, our proposed model outperformed all other models, resulting in improved breast lesion segmentation.

show abstract

“…The most important challenges in breast cancer detection process are accurate segmentation of the breast area and classification of the breast tissue, which play an important role in image guiding surgery, radiological treatment, and clinical computer-assisted diagnosis [2]. Several breast imaging modalities are currently being used for early detection of breast cancer such as ultrasound [3], mammography [4], MRI [5], thermography [6,7], etc. Computer aided detection (CAD) system is used for the diagnosis of breast cancer.…”

Section: Introductionmentioning

confidence: 99%

“…Computer aided detection (CAD) system is used for the diagnosis of breast cancer. This diagnosis contains several methods and techniques, including image processing, machine learning [7], data analysis, artificial intelligence [8], and deep learning [6,9,10].…”

Section: Introductionmentioning

confidence: 99%

A Novel CNN pooling layer for breast cancer segmentation and classification from thermograms

et al. 2022

View full text Add to dashboard Cite

Breast cancer is the second most frequent cancer worldwide, following lung cancer and the fifth leading cause of cancer death and a major cause of cancer death among women. In recent years, convolutional neural networks (CNNs) have been successfully applied for the diagnosis of breast cancer using different imaging modalities. Pooling is a main data processing step in CNN that decreases the feature maps’ dimensionality without losing major patterns. However, the effect of pooling layer was not studied efficiently in literature. In this paper, we propose a novel design for the pooling layer called vector pooling block (VPB) for the CCN algorithm. The proposed VPB consists of two data pathways, which focus on extracting features along horizontal and vertical orientations. The VPB makes the CNNs able to collect both global and local features by including long and narrow pooling kernels, which is different from the traditional pooling layer, that gathers features from a fixed square kernel. Based on the novel VPB, we proposed a new pooling module called AVG-MAX VPB. It can collect informative features by using two types of pooling techniques, maximum and average pooling. The VPB and the AVG-MAX VPB are plugged into the backbone CNNs networks, such as U-Net, AlexNet, ResNet18 and GoogleNet, to show the advantages in segmentation and classification tasks associated with breast cancer diagnosis from thermograms. The proposed pooling layer was evaluated using a benchmark thermogram database (DMR-IR) and its results compared with U-Net results which was used as base results. The U-Net results were as follows: global accuracy = 96.6%, mean accuracy = 96.5%, mean IoU = 92.07%, and mean BF score = 78.34%. The VBP-based results were as follows: global accuracy = 98.3%, mean accuracy = 97.9%, mean IoU = 95.87%, and mean BF score = 88.68% while the AVG-MAX VPB-based results were as follows: global accuracy = 99.2%, mean accuracy = 98.97%, mean IoU = 98.03%, and mean BF score = 94.29%. Other network architectures also demonstrate superior improvement considering the use of VPB and AVG-MAX VPB.

show abstract

Predicting Breast Cancer Leveraging Supervised Machine Learning Techniques

Cited by 22 publications

References 32 publications

Breast Cancer Risk Prediction with Stochastic Gradient Boosting

Breast Cancer Risk Prediction with Stochastic Gradient Boosting

Enhancing breast ultrasound segmentation through fine-tuning and optimization techniques: Sharp attention UNet

A Novel CNN pooling layer for breast cancer segmentation and classification from thermograms

Contact Info

Product

Resources

About