KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Subject: search.filters.subject.Breast Cancer

Search Results

Now showing 1 - 3 of 3
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Feature Selection Method Based on Hybrid Cuckoo Search and Firefly Algorithm for Breast Cancer Prediction
    (2025-01-01)
    Teerasarn, Chalanwich
    ;
    Kimpan, Warangkhana
    This research focuses on developing an efficient feature selection process using a hybrid technique combining Cuckoo Search algorithm and Firefly Algorithm. The Wisconsin Diagnostic Breast Cancer dataset is utilized to evaluate the capability of selecting significant features and eliminating irrelevant ones. The experimental results demonstrate that using the hybrid technique significantly improves the accuracy of machine learning compared to using the Cuckoo Search and Firefly Algorithm individually. Additionally, an analysis was conducted on the impact of splitting the dataset for training and testing, with splits of 70/30, 80/20, and 90/10. The experiments revealed a relationship between the number of selected features and the model accuracy. The findings from this study can serve as a guideline for developing appropriate feature selection process for complex data analysis problems.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Breast Cancer Detection using IR-UWB with Deep Learning
    (2021-01-01)
    Khumdee, Mawin
    ;
    Assawaroongsakul, Pongpol
    ;
    Phasukkit, Pattarapong
    ;
    Houngkamhang, Nongluck
    This paper proposes breast cancer positioning detection using the IR-UWB system with deep learning, which is an interesting alternative method. When compared to ultrasound, x-ray mammogram, and CT-scan, there are several advantages to using IR-UWB, including low cost, less energy required, less long-term effect, portability, and providing much more breast cancer screening access for patients. Nowadays, the IR-UWB system has many techniques for processing IR-UWB signals, and one of the most interesting technique is using deep learning. In this study, we collected data from nine IR-UWB antennas. Then, the prepared data is fed through Deep Neural Networks to find the hidden patterns of signal and predict the cancer position which are 16 of breast cancer positions and one of undetected, also known as 17 classes. The model gave an average accuracy up to 95.60%.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Cost-Sensitive Extreme Gradient Boosting for Imbalanced Classification of Breast Cancer Diagnosis
    (2020-08-01)
    Phankokkruad, Manop
    The clinical information can enhance the doctors for predicting and diagnosing the diseases also making the right decisions. Breast cancer is the most dangerous disease, early diagnosis can improve a chance of survival and can support clinical treatment. Detecting breast cancer takes a lot of time and it is hard to classification. However, the problem of the classification occurs when there is an unequal distribution of classes the dataset. This is caused by the low performance in the traditional machine learning models. For this reason, this work proposed the cost-sensitive XGBoost model, which is an improved version of the XGBoost model in conjunction with cost-sensitive learning. The models were applied to classify the four breast cancer datasets that contained the imbalanced data. In the experiment, this work determined the best parameters on each dataset by the hyperparameters optimization technique before configuring the models. The results indicated that the cost-sensitive XGBoost model had been skillful, and could improve classification accuracy in four datasets. In addition, this work evaluated the model performance by accuracy, ROC AUC, and k-Fold cross-validation to ensure that the new models is accurate.