• Miss. Pooja Mali
• Prof. Vilas. S. Gaikwad

The classification of breast cancer has been the subject of interest in the fields of healthcare and bioinformatics, because it is the second main reason of cancer-related deaths in women. Breast cancer can be analyzed using a biopsy where tissue is eliminated and studied under microscope. The identification of problem is based on the qualification and expe- rienced of the histopathologists, who will attention for abnormal cells. However, if the histopathologist is not well-trained or experienced, this may lead to wrong diagnosis. With the recent proposition in image processing and machine learning domain, there is an interest in experiment to develop a strong pattern recognition based framework to improve the quality of diagnosis. In this work, we will use the image feature extraction approach and machine learning approach for the classification of breast cancer using histology images into benign and malignant. Using Histopathological image we can preprocess this image after that apply feature extraction and classify the final result using SVM and Naive Bayes Classification techniques.

Histopathological image classification, breast cancer diagnose, feature extraction, SVM classification, Naive Bayes Classification;

"Advanced Machine Learning Technique for Classification of Histopathological Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.734-738, June-2019, Available :http://www.jetir.org/papers/JETIR1906D18.pdf

"Advanced Machine Learning Technique for Classification of Histopathological Images", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp734-738, June-2019, Available at : http://www.jetir.org/papers/JETIR1906D18.pdf