• Shruti Lohokare
• Priyanka Nikam
• Vrashali Talke
• Bhagyashri Wadkar

Breast cancer is foremost cause of death in women and many efforts have been needed to obstruct it. Early diagnosis crucially increases the chances of meticulous treatment and survival, but this process is very tedious and often assists to a controversy between pathologists. To conquer with is problem we develop breast cancer detection system based on histological images. Diagnosis of breast cancer mainly depends on perusal of biomedical images such as Mammograms, Histological images, etc. In this work, Convolutional Neural Network (CNN) is used which is a fruitful technique to detect breast cancer and classify the histological images into benign and malignant and its sub-classes like adenosis, fibroadenoma, etc. Each classification method has predefined work such as features extraction, selection of dataset and constructing model to perform the classification. This system can be considered as a second opinion for doctors.

" BREAST CANCER DIAGNOSISUSING NEURAL NETWORK BASED ON HISTOLOGY IMAGES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.670-674, May-2019, Available :http://www.jetir.org/papers/JETIR1905300.pdf

" BREAST CANCER DIAGNOSISUSING NEURAL NETWORK BASED ON HISTOLOGY IMAGES ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp670-674, May-2019, Available at : http://www.jetir.org/papers/JETIR1905300.pdf