Automatic Classification of BI-RADS in Mammography Reports Using Data Fusion

Background & aim: Breast cancer is one of the most common cancers in women and the main cause of death in cancer diseases, and mammography is the primary imaging method for early detection of breast masses. Rapid diagnosis with high accuracy is one of the serious concerns of doctors and healthcare centers when facing certain diseases, so the purpose of this article was to determine the automatic classification of BI-RADS in mammography reports using data fusion. Methods: The present descriptive, analytical, and retrospective study was conducted in 2023, the mammography report and the electronic file of the patients were extracted from the archiving and communication system of the patient's image and records obtained from the available information in the medical training center of Shahidzadeh hospital in Behbahan, Iran, which includes the mammography reports and the electronic record of 250 patients who had ample information. To model the proposed method using the collected dataset, Python software was used in the Visual Studio Code environment. Finally, cross-validation was used to evaluate the quality and validity of the results. Results: The results confirmed that the proposed approach, namely the use of Word2vec combined with TFIDF, and their integration with HIS, had a significant impact on the accuracy of medical text classification. The output vectors of Word2vec were used for BI-RADS level classification when TFIDF was applied or not applied, as well as with and without the integration of HIS, for classifiers such as CNN, MLP, DT, and k-NN, and the results were compared using evaluation measures such as accuracy, precision, sensitivity, positive predictive value, negative predictive value, and F1 score. The results indicated that the best accuracy with the proposed method using the multilayer perceptron classifier was 98.74%, but without HIS, the accuracy for the same classifier was 92.23%. Conclusion: By combining Word2vec with TFIDF, the accuracy of text classification could be increased, but the medical history of patients was important in the diagnosis of disease and could improve the accuracy. The results indicated that one should not focus only on medical reports and other clinical information and patients' history should also be used. Therefore, the use of HIS along with medical text reports could improve BI-RADS classification and have a positive effect on diagnosis and treatment processes.