• U. Krupakar Reddy
• Dr. P. Ratna Kamala
• G. Jyothi
• N. Himesh
• D. Hemalatha, M. Kamala

Among the most dangerous cancers that can affect women, breast cancer is one of the most common. For patients and their prognosis, the automatic diagnosis of breast cancer by studying histological images (HIS) is crucial. Clinical professionals can treat patients more effectively thanks to the classification of HIS, which gives them an accurate picture of disease. This paper's major goal is to use histology images to categorize the four different forms of breast cancer. Benign, in situ, Invasive, and Normal breast cancers make up the majority of cases. Using a convolutional neural network (CNN), it is possible to classify histology images accurately by the use of various layer kinds. Here, we're looking into a cutting-edge technique that can categorize breast cancer and translate the textual descriptions of the disease into auditory speech when picked up by a microphone.

Breast cancer, Convolutional Neural Network (CNN), Histological images, Breast cancer classification.

"CLASSIFICATION OF BREAST CANCER HISTOLOGY BY USING MATLAB", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.e599-e604, March-2023, Available :http://www.jetir.org/papers/JETIR2303476.pdf

"CLASSIFICATION OF BREAST CANCER HISTOLOGY BY USING MATLAB", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppe599-e604, March-2023, Available at : http://www.jetir.org/papers/JETIR2303476.pdf