Our research gives a proper insight on the use of deep learning and machine learning methods for the early diagnosis of breast cancer. In our paper we have found the efficiency through the algorithms by calculating the accuracy using a large amount of dataset related to medical imaging, clinical data and genetic information. From our research we show how the models improve the prediction power and effectiveness of breast cancer detection by inculcating the convolutional neural networks (CNNs), recurrent neural networks (RNNs), and other deep learning structure. Our results shows how artificial intelligence has the possibility to transform the breast cancer screening and its deep impact on early medication and patient outcomes. From our results we can see that the logistic regression has given the highest accuracy when compared with the other algorithms.
REFERENCES
1.
Islam
, M. M.
, Haque
, M. R.
, Iqbal
, H.
, Hasan
, M. M.
, Hasan
, M.
, & Kabir
, M. N.
(2020
). Breast cancer prediction: a comparative study using machine learning techniques
. SN Computer Science
, 1
, 1
–14
.2.
Kodipalli
, A.
, Guha
, S.
, Dasar
, S.
, & Ismail
, T.
(2022
). An inception-ResNet deep learning approach to classify tumours in the ovary as benign and malignant
. Expert Systems
, e13215
.3.
Ruchitha
, P. J.
, Richitha
, Y. S.
, Kodipalli
, A.
, & Martis
, R. J.
(2021
, December). Segmentation of Ovarian Cancer using Active Contour and Random Walker Algorithm
. In 2021 5th International Conference on Electrical, Electronics, Communication, Computer Technologies and Optimization Techniques (ICEECCOT)
(pp. 238
–241
). IEEE.4.
Kodipalli
, A.
, Devi
, S.
, Dasar
, S.
, & Ismail
, T.
(2022
). Segmentation and classification of ovarian cancer based on conditional adversarial image to image translation approach
. Expert Systems
, e13193
.5.
Ruchitha
, P. J.
, Sai
, R. Y.
, Kodipalli
, A.
, Martis
, R. J.
, Dasar
, S.
, & Ismail
, T.
(2022
, October). Comparative analysis of active contour random walker and watershed algorithms in segmentation of ovarian cancer
. In 2022 International Conference on Distributed Computing, VLSI, Electrical Circuits and Robotics (DISCOVER)
(pp. 234
–238
). IEEE.6.
Gururaj
, V.
, Ramesh
, S. V.
, Satheesh
, S.
, Kodipalli
, A.
, & Thimmaraju
, K.
(2022
). Analysis of deep learning frameworks for object detection in motion
. International Journal of Knowledge-based and Intelligent Engineering Systems
, 26
(1
), 7
–16
.7.
Guha
, S.
, Kodipalli
, A.
, & Rao
, T.
(2022
). Computational Deep Learning Models for Detection of COVID-19 Using Chest X-Ray Images. In Emerging Research in Computing, Information, Communication and Applications: Proceedings of ERCICA 2022
(pp. 291
–306
). Singapore
: Springer Nature Singapore
.8.
Rachana
, P. J.
, Kodipalli
, A.
, & Rao
, T.
(2022
). Comparison Between ResNet 16 and Inception V4 Network for COVID-19 Prediction. In Emerging Research in Computing, Information, Communication and Applications: Proceedings of ERCICA 2022
(pp. 283
–290
). Singapore
: Springer Nature Singapore
.9.
Zacharia
, S.
, & Kodipalli
, A.
(2022
). Covid Vaccine Adverse Side-Effects Prediction with Sequence-to-Sequence Model. In Emerging Research in Computing, Information, Communication and Applications: Proceedings of ERCICA 2022
(pp. 275
–281
). Singapore
: Springer Nature Singapore
.10.
Kodipalli
, A.
, Guha
, S.
, Dasar
, S.
, & Ismail
, T.
(2022
). An inception-ResNet deep learning approach to classify tumours in the ovary as benign and malignant
. Expert Systems
, e13215
.11.
Kodipalli
, A.
, Fernandes
, S. L.
, Dasar
, S. K.
, & Ismail
, T.
(2023
). Computational Framework of Inverted Fuzzy C-Means and Quantum Convolutional Neural Network Towards Accurate Detection of Ovarian Tumors
. International Journal of E-Health and Medical Communications (IJEHMC)
, 14
(1
), 1
–16
.12.
Kodipalli
, A.
(2018
). Cognitive architecture to analyze the effect of intrinsic motivation with metacognition over extrinsic motivation on swarm agents
. International Journal of Electrical and Computer Engineering
, 8
(5
), 3984
.13.
Kodipalli
, A.
, & Devi
, S.
(2021
). Prediction of PCOS and mental health using fuzzy inference and SVM
. Frontiers in Public Health
, 1804
.14.
Kodipalli
, A.
, & Devi
, S.
Analysis of fuzzy based intelligent health care application system for the diagnosis of mental health in women with ovarian cancer using computational models
. Intelligent Decision Technologies
, (Preprint), 1
–12
.15.
Singh
, G.
(2020
). Breast cancer prediction using machine learning
. Int. J. Sci. Res. Comput. Sci., Eng. Inf. Technol.
, 8
(4
), 278
–284
.
This content is only available via PDF.
© 2025 Author(s). Published under an exclusive license by AIP Publishing.
2025
Author(s)
You do not currently have access to this content.
