Experimental Investigation for Detecting Mitotic Cells in Medical Image using an Automated Algorithm

Main Article Content

Poli Lokeshwara Reddy
Sworna Kokila M L
K. Praveena
R. Jothilakshmi
D. Sugumar
Sudhir Ramadass
R.G. Vidhya

Abstract

Cancer of the breast is a malignant tumour that originates in the cells of the breast tissue. It is by far the most common kind of cancer found in females around the world, with a projected 2.3 million new cases will be discovered in the year 2020 alone. It is projected that one in eight women will be diagnosed with breast cancer at some point in their life, despite the fact that breast cancer can also occur in men. Breast cancer is a complex condition that can arise from a diverse set of factors, express itself in a variety of ways, and can be treated in a variety of ways. Ductal carcinoma in situ, invasive ductal carcinoma, and invasive lobular carcinoma are all different subtypes. Both the available treatment options and the expected outcome of breast cancer are very variable depending on the particular subtype of the illness. Breast cancer risk factors include drinking alcohol and not getting enough exercise, as well as getting older, having a family history of the disease, having genetic mutations, being exposed to estrogens, and having a family history of the disease. There is not always a connection between having risk factors and developing breast cancer, despite the fact that there can be a link between the two. The prognosis and treatment options for breast cancer are highly dependent on the stage of the disease at the time of diagnosis. During staging, the extent to which the cancer has spread throughout the body and how far it has progressed are both measured. The TNM system, the IAFCM system, the ACM system, and the MPIG system are just few of the staging systems that are used to classify breast cancer. These staging systems consider not only the size of the tumor but also whether or not lymph nodes are involved and whether or not distant metastases are present. The severity of breast cancer symptoms can vary widely, depending not only on the subtype of the disease but also on how far along it has progressed. Alterations in the size or shape of the breast, discharge from the nipple, and alterations in the skin of the breast (such as redness or dimpling) are all common indications. On the other hand, not all cases of breast cancer present themselves in a visible manner, and mammography and other forms of routine screening may be able to detect some of these cases. Options for treating breast cancer vary depending on the patient's condition and the stage of the disease, as well as the patient's overall health and their preferences towards therapy. Common examples of medical interventions include surgery, radiotherapy, chemotherapy, hormone therapy, and targeted therapy. Other examples include. In certain cases, it may be appropriate to participate in more than one form of treatment.

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How to Cite
Reddy, P. L. ., M L, S. K. ., Praveena, K. ., Jothilakshmi, R. ., Sugumar, D. ., Ramadass, S. ., & Vidhya, R. (2023). Experimental Investigation for Detecting Mitotic Cells in Medical Image using an Automated Algorithm. International Journal on Recent and Innovation Trends in Computing and Communication, 11(9s), 129–138. https://doi.org/10.17762/ijritcc.v11i9s.7404
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References

Acton, ST & Mukherjee, DP 2000, ‘Scale space classification using area morphology’, IEEE Trans. Image Process, vol. 9, no. 4, pp. 623-63.

N.K Anushkannan, Vijaya R. Kumbhar, Suresh Kumar Maddila, Chandra Sekhar Kolli, B.Vidhya, R.G.Vidhya (2022) YOLO Algorithm for Helmet Detection in Industries for Safety Purpose. DOI: 10.1109/ICOSEC54921.2022.9952154

Vikas Somani, A. Nisam Rahman, Devvret Verma, Radha Raman Chandan, R.G. Vidhya, Vinodh P Vijayan (2022) Classification of Motor Unit Action Potential Using Transfer Learning for the Diagnosis of Neuromuscular Diseases. DOI: 10.1109/ICSSS54381.2022.9782209.

Sivasankari S S, J. Surendiran, N. Yuvaraj, M. Ramkumar, C.N. Ravi, R.G Vidhya (2022) Classification of Diabetes using Multilayer Perceptron. DOI: 10.1109/ICDCECE53908.2022.9793085

K. Srinivasa Reddy,Vinodh P Vijayan, Ayan Das Gupta, Prabhdeep Singh, R.G. Vidhya, Dhiraj Kapila (2022) Implementation of Super Resolution in Images Based on Generative Adversarial Network. DOI: 10.1109/ICSSS54381.2022.9782170

K. Sivanagireddy, Srinivas Yerram, S. Sri Nandhini Kowsalya, S.S. Sivasankari, J. Surendiran, R.G. Vidhya (2022) Early Lung Cancer Prediction using Correlation and Regression. DOI: 10.1109/ICCPC55978.2022.10072059.

R.G. Vidhya, J. Seetha, Sudhir Ramadass, S. Dilipkumar, Ajith Sundaram, G. Saritha (2022) An Efficient Algorithm to Classify the Mitotic Cell using Ant Colony Algorithm.

DOI :10.1109/ICCPC55978.2022.10072277

R.G. Vidhya, V Bhoopathy, Mohammad Shahid Kamal, Arvind Kumar Shukla, Gururaj T, Thulasimani T (2022) Smart Design and Implementation of home Automation System using WIFI. DOI: 10.1109/ICAISS55157.2022.10010792

D. Sengeni, Muthuraman A, Naresh Vurukonda, G. Priyanka, Priyanka Suram, R. G Vidhya (2022) A Switching Event-Triggered Approach to Proportional Integral Synchronization Control for Complex Dynamical Networks. DOI: 10.1109/ICECAA55415.2022.9936124

Isabella Rossi, Reinforcement Learning for Resource Allocation in Cloud Computing , Machine Learning Applications Conference Proceedings, Vol 1 2021.

Anna, G., Hernandez, M., García, M., Fernández, M., & González, M. Optimizing Course Recommendations for Engineering Students Using Machine Learning. Kuwait Journal of Machine Learning, 1(1). Retrieved from http://kuwaitjournals.com/index.php/kjml/article/view/104

Bent AL-Huda Sahib Ghetran, Enas Abdul Hafedh Mohammed. (2023). Bayes Estimation of Parameters of the Kibble-Bivariate Gamma Distribution Under A Precautionary Loss Function for Fuzzy Data Using Simulation. International Journal of Intelligent Systems and Applications in Engineering, 11(2s), 373–380. Retrieved from https://ijisae.org/index.php/IJISAE/article/view/2733

R.G. Vidhya, B Kezia Rani, Kamlesh Singh, D. Kalpanadevi, Jyothi Prasad Patra, T. Aditya Sai Srinivas (2022) An Effective Evaluation of SONARS using Arduino and Display on Processing IDE. DOI: 10.1109/ICCPC55978.2022.10072229

R.G. Vidhya, Kamlesh Singh, P John Paul, T. Aditya Sai Srinivas, Jyoti Prasad Patra, K.V.Daya Sagar (2022) Smart Design and Implementation of Self Adjusting Robot using Arduino. DOI: 10.1109/ICAISS55157.2022.10011083

Deshpande, V. (2021). Layered Intrusion Detection System Model for The Attack Detection with The Multi-Class Ensemble Classifier . Machine Learning Applications in Engineering Education and Management, 1(2), 01–06. Retrieved from http://yashikajournals.com/index.php/mlaeem/article/view/10

R.G. Vidhya, J Surendiran, G. Saritha (2022) Machine Learning Based Approach to Predict the Position of Robot and its Application. DOI: 10.1109/ICCPC55978.2022.10072031

R.G. Vidhya, T.S. Sasikala, Ayoobkhan Mohamed Uvaze Ahamed, Subair Ali Liayakath Ali Khan, Kamlesh Singh, M. Saratha (2022) Classification and Segmentation of Mitotic Cells using Ant Colony Algorithm and TNM Classifier. DOI: 10.1109/ICAISS55157.2022.10010914

J. Surendiran, K.Dinesh Kumar, T. Sathiya, S.S. Sivasankari, R.G. Vidhya, N. Balaji (2022) Prediction of Lung Cancer at Early Stage Using Correlation Analysis and Regression Modelling. DOI: 10.1109/CCIP57447.2022.10058630

Dr. Sandip Kadam. (2014). An Experimental Analysis on performance of Content Management Tools in an Organization. International Journal of New Practices in Management and Engineering, 3(02), 01 - 07. Retrieved from http://ijnpme.org/index.php/IJNPME/article/view/27

D Shekar Goud;Vineetha Varghese;Komal B Umare;J. Surendiran, R.G. Vidhya, K Sathish, (2022) Internet of Things-based infrastructure for the accelerated charging of electric vehicles. DOI: 10.1109/ICCPC55978.2022.10072086

R. G.Vidhya, R. Saravanan, K.Rajalakshmi (2020) Mitosis Detectio for Breast Cancer Grading , International Journal of Advanced Science and Technology. 29: 4478-4485

Sivasankari S S, et. al. Classification of Diabetes using Multilayer Perceptron. DOI: 10.1109/ICDCECE53908.2022.9793085

Al-Kofahi, Y, Lassoued, W, Lee, W & Roysam, B 2010, ‘Improvedautomatic detection and segmentation of cell nuclei in histopathology images’, IEEE Trans. Biomed. Eng, vol. 57, no. 4, pp. 841-852..

Seyedhosseini, M & Tasdizen, T 2013, ‘Multi-class multi-scale series contextual model for image segmentation’, IEEE Trans. Image Process, vol. 22, no. 11, pp. 4486-4496.

Yang, X, Li, H & Zhou, X 2006, ‘Nuclei segmentation using markercontrolled watershed, tracking using mean-shift, and Kalman filter in time-lapse microscopy’, IEEET Rans. Circuits Syst. I, Reg, Papers, vol. 53, no. 11, pp. 2405-2414.

Dalle, JR, Leow, WK, Racoceanu, D, Tutac, AE & Putti, TC 2008,Automatic breast cancer grading of histopathological images?, in Proc. 30th Annu. Int. Conf. IEEE Eng. Med. Biol. Soc. (EMBS), pp. 3052-3055.

Demir, C & Yener, B 2005, „Automated cancer diagnosis based on histopathological images: A systematic survey?, Dept. Comput. Sci, Rensselaer Polytech. Inst, Troy, NY, USA, Tech. Rep. TR-05-09. Elston, CW, Ellis, IO & Pinder, SE 1999, „Pathological prognostic factors in breast cancer?, Critical Reviews in Oncology/Hematology, vol. 31, no. 3, pp. 209- 223.

Frierson, HF Jr, 1995, „Interobserver reproducibility of the Nottingham modification of the bloom and Richardson histologic grading scheme for infiltrating ductal carcinoma?, Amer. J. Clin. Pathol, vol. 103, no. 2, pp. 195-198.