This automated technology may well be useful in further assisting with other problems resulting from physical fatigue, poor mammogram image quality, inconsistent FNA discriminator numerical assignments, as well as other possible sensor interpretation problems. The purpose of this proposed session is to present current and ongoing research in the CAD of breast carcinoma. To establish that CAD tools are simple and economical to implement in the clinical setting To demonstrate that CAD tools can provide the cytophathogists, radiologists and neurosurgeons with an early diagnosis of breast cancer that is accurate, consistent and efficient as well as accessible. Patient age was also included. The existence or absence of malignancy was confirmed in each case via open surgical biopsy.
Results indicate the ability for small linear models to also provide reasonable discrimination.
Sensitivity analysis also indicates the potential for understanding the networks response to various input features. These projects share in common the use of feedforward, error backpropagation ANNs. Inputs to the ANNs are medical findings such as mammographic or ultrasound lesion descriptors and patient history data. All ANNs undergo supervised training using actual patient data. We have developed ANNs, which have the ability to predict the outcome of breast biopsy at a level comparable or better than expert radiologists.