Abstract:
Breast cancer is an invasive tumor that develops in the breast tissue. It is the most common
cancer and also the leading cause of cancer mortality among females worldwide. Survival from
breast cancer can be increased through advances in screening methods and early diagnosis.
Clinical examination, screening using imaging modalities and pathological assessment (biopsy
test) are common methods of breast cancer diagnosis. Among these, pathological assessment can
be taken as a gold standard due to its potential in identifying the cancer type, grade and stage.
However, current diagnosis using biopsy test is commonly done through visual inspection. This
manual diagnosis is time consuming, tedious and subjective which may lead to misdiagnosis.
Different machine learning and deep learning methods have been proposed in the literature to
automate the manual breast cancer diagnosis mechanism. To our knowledge, an integrated
system that can classify breast cancer to its subtypes and identification of the cancer grade from
biopsy tests has not been previously done. Identification of breast subtypes and grade is essential
for understanding the biological characteristics and clinical behavior as well as for developing
personalized treatments. Moreover, the results claimed from previously proposed automation
techniques are less accurate and unreliable.
In this thesis, an automatic detection of breast cancer type, subtype and grade was proposed
based on deep learning neural network model. The system was developed using python software.
It was trained and validated using histopathological images acquired from „break-his dataset‟, the
„zendo online dataset‟ and local data acquired from Jimma University Medical Center (JUMC),
by using optikam PRO5 digital camera and optika microscope. All images have been preprocessed using CLAHE algorithm and histogram matching techniques, prior to feeding to the
ResNet 50 pre-trained model. The developed system is capable of classifying breast cancer into
binary classes (benign and malignant) and multi-classes (sub-types). Identification of cancer
grade is done for cancers that are classified as malignant ductal carcinomas. Our test results
showed that, the proposed method is 96.75%, 96.7%, 95.78%, and 93.86 % accurate for binary
classification, benign sub-type classification, malignant sub-type classification, and grade
identification, respectively.
