APA Style
Gollangi, H. K. , Gollangi, H. K. Bauskar, S. R. , Bauskar, S. R. Madhavaram, C. R. , Madhavaram, C. R. Galla, E. P. , Galla, E. P. Sunkara, J. R. , & Sunkara, J. R. (2021). Exploring AI Algorithms for Cancer Classification and Prediction Using Electronic Health Records.
Current Research in Public Health, 1(1), 65-74.
https://doi.org/10.31586/jaibd.2020.1109
ACS Style
Gollangi, H. K. ; Gollangi, H. K. Bauskar, S. R. ; Bauskar, S. R. Madhavaram, C. R. ; Madhavaram, C. R. Galla, E. P. ; Galla, E. P. Sunkara, J. R. ; Sunkara, J. R. Exploring AI Algorithms for Cancer Classification and Prediction Using Electronic Health Records.
Current Research in Public Health 2021 1(1), 65-74.
https://doi.org/10.31586/jaibd.2020.1109
Chicago/Turabian Style
Gollangi, Hemanth Kumar, Hemanth Kumar Gollangi. Sanjay Ramdas Bauskar, Sanjay Ramdas Bauskar. Chandrakanth Rao Madhavaram, Chandrakanth Rao Madhavaram. Eswar Prasad Galla, Eswar Prasad Galla. Janardhana Rao Sunkara, and Janardhana Rao Sunkara. 2021. "Exploring AI Algorithms for Cancer Classification and Prediction Using Electronic Health Records".
Current Research in Public Health 1, no. 1: 65-74.
https://doi.org/10.31586/jaibd.2020.1109
AMA Style
Gollangi HK, Gollangi HKBauskar SR, Bauskar SRMadhavaram CR, Madhavaram CRGalla EP, Galla EPSunkara JR, Sunkara JR. Exploring AI Algorithms for Cancer Classification and Prediction Using Electronic Health Records.
Current Research in Public Health. 2021; 1(1):65-74.
https://doi.org/10.31586/jaibd.2020.1109
@Article{crph1109,
AUTHOR = {Gollangi, Hemanth Kumar and Bauskar, Sanjay Ramdas and Madhavaram, Chandrakanth Rao and Galla, Eswar Prasad and Sunkara, Janardhana Rao and Reddy, Mohit Surender},
TITLE = {Exploring AI Algorithms for Cancer Classification and Prediction Using Electronic Health Records},
JOURNAL = {Current Research in Public Health},
VOLUME = {1},
YEAR = {2021},
NUMBER = {1},
PAGES = {65-74},
URL = {https://www.scipublications.com/journal/index.php/JAIBD/article/view/1109},
ISSN = {2831-5162},
DOI = {10.31586/jaibd.2020.1109},
ABSTRACT = {Cell division that is not controlled leads to cancer, an incurable condition. An early diagnosis has the potential to lower death rates from breast cancer, the most frequent disease in women worldwide. Imaging studies of the breast may help doctors find the disease and diagnose it. This study explores an effectiveness of DL and ML models in a classification of mammography images for breast cancer detection, utilizing the publicly available CBIS-DDSM dataset, which comprises 5,000 images evenly divided between benign and malignant cases. To improve diagnostic accuracy, models such as Gaussian Naïve Bayes (GNB), CNNs, KNN, and MobileNetV2 were assessed employing performance measures including F1-score, recall, accuracy, and precision. The methodology involved data preprocessing techniques, including transfer learning and feature extraction, followed by data splitting for robust model training and evaluation. Findings indicate that MobileNetV2 achieved a highest accuracy99.4%, significantly outperforming GNB (87.2%), CNN (96.7%), and KNN (91.2%). The outstanding capacity of MobileNetV2 to identify between benign and malignant instances was shown by the investigation, which also made use of confusion matrices and ROC curves to evaluate model performance.},
}
%0 Journal Article
%A Gollangi, Hemanth Kumar
%A Bauskar, Sanjay Ramdas
%A Madhavaram, Chandrakanth Rao
%A Galla, Eswar Prasad
%A Sunkara, Janardhana Rao
%A Reddy, Mohit Surender
%D 2021
%J Current Research in Public Health
%@ 2831-5162
%V 1
%N 1
%P 65-74
%T Exploring AI Algorithms for Cancer Classification and Prediction Using Electronic Health Records
%M doi:10.31586/jaibd.2020.1109
%U https://www.scipublications.com/journal/index.php/JAIBD/article/view/1109
TY - JOUR
AU - Gollangi, Hemanth Kumar
AU - Bauskar, Sanjay Ramdas
AU - Madhavaram, Chandrakanth Rao
AU - Galla, Eswar Prasad
AU - Sunkara, Janardhana Rao
AU - Reddy, Mohit Surender
TI - Exploring AI Algorithms for Cancer Classification and Prediction Using Electronic Health Records
T2 - Current Research in Public Health
PY - 2021
VL - 1
IS - 1
SN - 2831-5162
SP - 65
EP - 74
UR - https://www.scipublications.com/journal/index.php/JAIBD/article/view/1109
AB - Cell division that is not controlled leads to cancer, an incurable condition. An early diagnosis has the potential to lower death rates from breast cancer, the most frequent disease in women worldwide. Imaging studies of the breast may help doctors find the disease and diagnose it. This study explores an effectiveness of DL and ML models in a classification of mammography images for breast cancer detection, utilizing the publicly available CBIS-DDSM dataset, which comprises 5,000 images evenly divided between benign and malignant cases. To improve diagnostic accuracy, models such as Gaussian Naïve Bayes (GNB), CNNs, KNN, and MobileNetV2 were assessed employing performance measures including F1-score, recall, accuracy, and precision. The methodology involved data preprocessing techniques, including transfer learning and feature extraction, followed by data splitting for robust model training and evaluation. Findings indicate that MobileNetV2 achieved a highest accuracy99.4%, significantly outperforming GNB (87.2%), CNN (96.7%), and KNN (91.2%). The outstanding capacity of MobileNetV2 to identify between benign and malignant instances was shown by the investigation, which also made use of confusion matrices and ROC curves to evaluate model performance.
DO - Exploring AI Algorithms for Cancer Classification and Prediction Using Electronic Health Records
TI - 10.31586/jaibd.2020.1109
ER -
