A novel classification scheme to decline the mortality rate among women due to breast tumor

B Mughal, M Sharif, N Muhammad… - … research and technique, 2018 - Wiley Online Library
Early screening of skeptical masses or breast carcinomas in mammograms is supposed to
decline the mortality rate among women. This amount can be decreased more on …

Characterization of Mueller matrix elements for classifying human skin cancer utilizing random forest algorithm

NT Luu, TH Le, QH Phan… - Journal of biomedical …, 2021 - spiedigitallibrary.org
Significance: The Mueller matrix decomposition method is widely used for the analysis of
biological samples. However, its presumed sequential appearance of the basic optical …

Development and validation of a predictive radiomics model for clinical outcomes in stage I non-small cell lung cancer

W Yu, C Tang, BP Hobbs, X Li, EJ Koay… - International Journal of …, 2018 - Elsevier
Purpose To develop and validate a radiomics signature that can predict the clinical
outcomes for patients with stage I non-small cell lung cancer (NSCLC). Methods and …

[HTML][HTML] An effective ensemble machine learning approach to classify breast cancer based on feature selection and lesion segmentation using preprocessed …

AKMRH Rafid, S Azam, S Montaha, A Karim, KU Fahim… - Biology, 2022 - mdpi.com
Simple Summary The screening of breast cancer in its earlier stages can play a crucial role
in minimizing mortality rate by enabling clinicians to administer timely treatments and …

Two-way threshold-based intelligent water drops feature selection algorithm for accurate detection of breast cancer

DJ Kalita, VP Singh, V Kumar - Soft Computing, 2022 - Springer
Breast cancer is one of the common reasons for deaths of women over the globe. It has
been found that a Computer-Aided Diagnosis (CAD) system can be designed using X-ray …

Covid-19 classification based on gray-level co-occurrence matrix and support vector machine

Y Chen - COVID-19: Prediction, Decision-Making, and its …, 2021 - Springer
Covid-19 is a new epidemic recently. Early diagnosis of related diseases relies on the
analysis of the patient's clinical symptoms and kit testing. To identify this disease efficiently …

Effective mammogram classification based on center symmetric-LBP features in wavelet domain using random forests

VP Singh, S Srivastava… - Technology and Health …, 2017 - journals.sagepub.com
Mammogram classification is a crucial and challenging problem, because it helps in early
diagnosis of breast cancer and supports radiologists in their decision to analyze similar …

Spatial Bayesian modeling of GLCM with application to malignant lesion characterization

X Li, M Guindani, CS Ng, BP Hobbs - Journal of applied statistics, 2019 - Taylor & Francis
The emerging field of cancer radiomics endeavors to characterize intrinsic patterns of tumor
phenotypes and surrogate markers of response by transforming medical images into objects …

Detection of breast cancer through mammogram using wavelet-based LBP features and IWD feature selection technique

DJ Kalita, VP Singh, V Kumar - SN Computer Science, 2022 - Springer
Breast cancer is as one of the common reasons of deaths in women. To detect this cancer in
early stage, a computer-aided diagnosis (CAD) system can be designed using X-ray …

Detection and classification of breast cancer in mammogram images using entropy-based Fuzzy C-Means Clustering and RMCNN

R Kalam, C Thomas - Multimedia Tools and Applications, 2024 - Springer
Radiologists employ mammograms for the detection of breast cancer in patients, particularly
as breast cancer exhibits higher incidence rates in women. Early identification of breast …