پیوندهای مفید
اخبار و اعلانات
آمار
| تعداد نشریات | 158 |
| تعداد شمارهها | 6,241 |
| تعداد مقالات | 67,872 |
| تعداد مشاهده مقاله | 115,474,993 |
| تعداد دریافت فایل اصل مقاله | 90,244,314 |
Breast Cancer Classification through Meta-Learning Ensemble Model based on Deep Neural Networks | ||
| Journal of Information Technology Management | ||
| دوره 16، شماره 1، 2024، صفحه 61-87 اصل مقاله (1.42 M) | ||
| نوع مقاله: Research Paper | ||
| شناسه دیجیتال (DOI): 10.22059/jitm.2024.96375 | ||
| نویسندگان | ||
| Sivaji U1؛ Siva Padmini P2؛ Chatrapathy K3؛ K Arun Kumar4؛ Kutralakani Chanthirasekaran5؛ Vonguru Chandraprakash6؛ Yadala Sucharitha* 7 | ||
| 1Department of Information Technology, Institute of Aeronautical Engineering, Dundigal, Hyderabad, Telangana, India. | ||
| 2Computer Science and Engineering (Data Science), Marri Laxman Reddy Institute of Technology and Management, Dundigal, Hyderabad, Telangana, India. | ||
| 3School of Computing &Information Technology, REVA University, Bangalore (North), Karnataka, India. | ||
| 4Computer Science and Engineering, GITAM School of Technology GITAM University, Bengaluru, India. | ||
| 5Department of Electronics and Communication Engineering, Saveetha Engineering College, Chennai, India. | ||
| 6Department of Information Technology, St. Martin’s Engineering College, Hyderabad, India. | ||
| 7Computer Science and Engineering (Data Science), VNR Vignana Jyothi Institute of Engineering and Technology, Hyderabad, Telangana, India. | ||
| چکیده | ||
| Predicting the development of cancer has always been a serious challenge for scientists and medical professionals. The prompt identification and prognosis of a disease is greatly aided by early-stage detection. Researchers have proposed a number of different strategies for early cancer detection. The purpose of this research is to use meta-learning techniques and several different kinds of convolutional-neural-networks(CNN) to create a model that can accurately and quickly categorize breast cancer(BC). There are many different kinds of breast lesions represented in the Breast Ultrasound Images (BUSI) dataset. It is essential for the early diagnosis and treatment of BC to determine if these tumors are benign or malignant. Several cutting-edge methods were included in this study to create the proposed model. These methods included meta-learning ensemble methodology, transfer-learning, and data-augmentation. With the help of meta-learning, the model will be able to swiftly learn from novel data sets. The feature extraction capability of the model can be improved with the help of pre-trained models through a process called transfer learning. In order to have a larger and more varied dataset, we will use data augmentation techniques to produce new training images. The classification accuracy of the model can be enhanced by using meta-ensemble learning techniques to aggregate the results of several CNNs. Ensemble-learning(EL) will be utilized to aggregate the results of various CNN, and a meta-learning strategy will be applied to optimize the learning process. The evaluation results further demonstrate the model's efficacy and precision. Finally, the suggested model's accuracy, precision, recall, and F1-score will be contrasted to those of conventional methods and other current systems. | ||
| کلیدواژهها | ||
| Deep-Learning؛ Meta-Learning, EL, CNN, Breast-Cancer, Classification | ||
| مراجع | ||
|
Abhisheka, B.; Biswas, S. K. & Purkayastha, B. (2023). A comprehensive review on breast cancer detection, classification and segmentation using deep learning. Archives of Computational Methods in Engineering, 1-30.
Agrawal, A. (2023). Classification and Detection of Brain Tumors by Aquila Optimizer Hybrid Deep Learning Based Latent Features with Extreme Learner. In ITM Web of Conferences (53). EDP Sciences.
Ahmed, S. T.; Singh, D. K.; Basha, S. M.; Abouel Nasr, E.; Kamrani, A. K.; & Aboudaif, M. K. (2021). Neural network based mental depression identification and sentiments classification technique from speech signals: A COVID-19 Focused Pandemic Study. Frontiers in public health, 9, 781827.
Ashreetha, B.; Devi, M. R.; Kumar, U. P.; Mani, M. K.; Sahu, D. N. & Reddy, P. C. S. (2022). Soft optimization techniques for automatic liver cancer detection in abdominal liver images. International journal of health sciences, 6.
Chanchal, A. K.; Lal, S.; Barnwal, D.; Sinha, P.; Arvavasu, S. & Kini, J. (2023). Evolution of LiverNet 2. x: Architectures for automated liver cancer grade classification from H&E stained liver histopathological images. Multimedia Tools and Applications, 1-31.
Chillakuru, P.; Madiajagan, M.; Prashanth, K. V.; Ambala, S.; Shaker Reddy, P. C. & Pavan, J. (2023). Enhancing wind power monitoring through motion deblurring with modified GoogleNet algorithm. Soft Computing, 1-11.
Cui, C.; Yang, H.; Wang, Y.; Zhao, S.; Asad, Z.; Coburn, L. A. & Huo, Y. (2023). Deep multi-modal fusion of image and non-image data in disease diagnosis and prognosis: a review. Progress in Biomedical Engineering.
Das, A. & Mohanty, M. N. (2022). Design of ensemble recurrent model with stacked fuzzy ARTMAP for breast cancer detection. Applied Computing and Informatics.
Dash, P. B.; Behera, H. S. & Senapati, M. R. (2022). Breast Cancer Mammography Identification with Deep Convolutional Neural Network. In Computational Intelligence in Data Mining: Proceedings of ICCIDM 2021 (pp. 741-752). Singapore: Springer Nature Singapore.
de Oliveira, C. I.; do Nascimento, M. Z.; Roberto, G. F.; Tosta, T. A.; Martins, A. S. & Neves, L. A. (2023). Hybrid models for classifying histological images: An association of deep features by transfer learning with ensemble classifier. Multimedia Tools and Applications, 1-24.
Deivasigamani, S.; Rani, A.J.M.; Natchadalingam, R.; Vijayakarthik, P.; Kumar, G.B.S. and Reddy, P.C.S. (2023), August. Crop Yield Prediction Using Deep Reinforcement Learning. In 2023 Second International Conference on Trends in Electrical, Electronics, and Computer Engineering (TEECCON) (pp. 137-142). IEEE.
Himavarnika, A. & Prasanthi, P. (2023). A Statistical Modelling to Detect Carcinoma Cancer in Its Incipient Stages in Healthcare. Journal of Coastal Life Medicine, 11, 468-481.
Iqbal, M. S.; Ahmad, W.; Alizadehsani, R.; Hussain, S. & Rehman, R. (2022, November). Breast Cancer Dataset, Classification and Detection Using Deep Learning. In Healthcare (10) 12, 2395.
Jabeen, K.; Khan, M. A.; Alhaisoni, M.; Tariq, U.; Zhang, Y. D.; Hamza, A. & Damaševičius, R. (2022). Breast cancer classification from ultrasound images using probability-based optimal deep learning feature fusion. Sensors, 22(3), 807.
Jakhar, A. K.; Gupta, A. & Singh, M. (2023). SELF: a stacked-based ensemble learning framework for breast cancer classification. Evolutionary Intelligence, 1-16.
Kumar, G. R.; Reddy, R. V.; Jayarathna, M.; Pughazendi, N.; Vidyullatha, S. & Reddy, P. C. S. (2023). Web application based Diabetes prediction using Machine Learning. In 2023 International Conference on Advances in Computing, Communication and Applied Informatics (ACCAI) pp. 1-7, IEEE.
Kumar, K.; Pande, S.V.; Kumar, T.; Saini, P.; Chaturvedi, A.; Reddy, P.C.S. & Shah, K.B. (2023). Intelligent controller design and fault prediction using machine learning model. International Transactions on Electrical Energy Systems, 2023.
Kumar, S. S.; Ahmed, S. T.; Xin, Q.; Sandeep, S.; Madheswaran, M. & Basha, S. M. (2022). Unstructured Oncological Image Cluster Identification Using Improved Unsupervised Clustering Techniques. Computers, Materials & Continua, 72(1).
Latha, S. B.; Dastagiraiah, C.; Kiran, A.; Asif, S.; Elangovan, D. & Reddy, P. C. S. (2023, August). An Adaptive Machine Learning model for Walmart sales prediction. In 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT) (pp. 988-992). IEEE.
LK, S. S.; Ahmed, S. T.; Anitha, K. & Pushpa, M. K. (2021, November). COVID-19 outbreak based coronary heart diseases (CHD) prediction using SVM and risk factor validation. In 2021 Innovations in Power and Advanced Computing Technologies (i-PACT) (pp. 1-5). IEEE.
Lokesh, S.; Priya, A.; Sakhare, D. T.; Devi, R. M.; Sahu, D. N. & Reddy, P. C. S. (2022). CNN based deep learning methods for precise analysis of cardiac arrhythmias. International journal of health sciences, 6.
Madhavi, G. B.; Bhavani, A. D.; Reddy, Y. S.; Kiran, A.; Chitra, N. T. & Reddy, P. C. S. (2023, June). Traffic Congestion Detection from Surveillance Videos using Deep Learning. In 2023 International Conference on Computer, Electronics & Electrical Engineering & their Applications (IC2E3) pp. 1-5, IEEE.
Muduli, D.; Kumar, R. R.; Pradhan, J. & Kumar, A. (2023). An empirical evaluation of extreme learning machine uncertainty quantification for automated breast cancer detection. Neural Computing and Applications, 1-16.
Muthappa, K. A.; Nisha, A. S. A.; Shastri, R.; Avasthi, V. & Reddy, P. C. S. (2023). Design of high-speed, low-power non-volatile master slave flip flop (NVMSFF) for memory registers designs. Applied Nanoscience, 1-10.
Nemade, V.; Pathak, S. & Dubey, A. K. (2023). Deep learning-based ensemble model for classification of breast cancer. Microsystem Technologies, 1-15.
Nomani, A.; Ansari, Y.; Nasirpour, M. H.; Masoumian, A.; Pour, E. S. & Valizadeh, A. (2022). PSOWNNs-CNN: a computational radiology for breast cancer diagnosis improvement based on image processing using machine learning methods. Computational Intelligence and Neuroscience, 2022.
PACAL, İ. (2022). Deep learning approaches for classification of breast cancer in ultrasound (US) images. Journal of the Institute of Science and Technology, 12(4), 1917-1927.
Pathan, R. K.; Alam, F. I.; Yasmin, S.; Hamd, Z. Y.; Aljuaid, H.; Khandaker, M. U. & Lau, S. L. (2022, November). Breast Cancer Classification by Using Multi-Headed Convolutional Neural Network Modeling. In Healthcare p. 2367. MDPI.
Patra, A.; Behera, S. K.; Barpanda, N. K. & Sethy, P. K. (2022). Effect of Microscopy Magnification Towards Grading of Breast Invasive Carcinoma: An Experimental Analysis on Deep Learning and Traditional Machine Learning Methods. Ingénierie des Systèmes d'Information, 27(4).
Rana, M. & Bhushan, M. (2023). Classifying breast cancer using transfer learning models based on histopathological images. Neural Computing and Applications, 35(19), 14243-14257.
Rao, K. R.; Prasad, M. L.; Kumar, G. R.; Natchadalingam, R.; Hussain, M. M. & Reddy, P. C. S. (2023, August). Time-Series Cryptocurrency Forecasting Using Ensemble Deep Learning. In 2023 International Conference on Circuit Power and Computing Technologies (ICCPCT) (pp. 1446-1451). IEEE.
Sucharitha, Y.; Reddy, P. C. S. & Chitti, T. N. (2023, July). Deep learning based framework for crop yield prediction. In AIP Conference Proceedings 1 (2548), AIP Publishing.
Suneel, S.; Balaram, A.; Amina Begum, M.; Umapathy, K.; Reddy, P. C. S. & Talasila, V. (2024). Quantum mesh neural network model in precise image diagnosing. Optical and Quantum Electronics, 56(4), 559.
Yadala, S.; Pundru, C. S. R. & Solanki, V. K. (2023, March). A Novel Private Encryption Model in IoT Under Cloud Computing Domain. In The International Conference on Intelligent Systems & Networks pp. 263-270. Singapore: Springer Nature Singapore.
Zahoor, S.; Shoaib, U. & Lali, I. U. (2022). Breast cancer mammograms classification using deep neural network and entropy-controlled whale optimization algorithm. Diagnostics, 12(2), 557. | ||
|
آمار تعداد مشاهده مقاله: 141 تعداد دریافت فایل اصل مقاله: 122 |
||