Abstract:
Breast cancer and cervical cancer are two of the most common and
deadly malignancies in women. Early diagnosis and treatment can save lives and
improve quality of life. However, there is a shortage of pathologists and physicians
in most developing countries, including Ethiopia, preventing many breast and cer vical cancer patients from early cancer screening. Many women, particularly in
low resource settings, have limited access to early diagnosis of breast and cervical
cancer and receive poor treatment which in turn increases the morbidity and mor tality due to these cancers. In this paper, an integrated intelligent decision support
system is proposed for the diagnosis and management of breast and cervical cancer
using multimodal im-age data. The system includes breast cancer type, sub-type
and grade classification, cervix type (transformation zone) detection and classifi cation, pap smear image classification, and histopathology-based cervical cancer
type classification. In addition, patient registration, data retrieval, and storage as
well as cancer statistical analysis mechanisms are integrated into the proposed
system. A ResNet152 deep learning model was used for classification tasks and
satisfactory results were achieved when testing the model. The developed system
was deployed to an offline web page which has added the advantage of storing the
digital medical images and the labeled results for future use by the physicians or
other researchers
