Transforming Hematological Data Interpretation: A Deep Learning and NLP Framework for Blood Cancer Prognostics

In this research work, we have investigated the transformative potential of a deep learning and natural language processing framework in enriching the capabilities to prognosticate blood cancer. Specifically, we used a dataset that covers a complete range of a clinical, high-resolution blood smear, bone marrow biopsy and imaging such as CT, MRI and PET scans. To achieve our objectives, we also designed and implemented hybrid models of CNNs and LSTM networks, specifically VGG 19 + LSTM, VGG 16 + LSTM and ResNet 50 + LSTM. The results of the study showed that the VGG 19 + LSTM model achieved the highest accuracy than the VGG 16 + LSTM and ResNet 50 + LSTM models. The confusion matrix, precision, recall and F1 score metrics gave a clear indication of the fact that VGG 19 + LSTM has the highest capacity predictive capabilities. During the calculation of the confusion matrix, this model showed the minimum number of false positives and false negatives. We also noticed that all models converge during the loss and accuracy in every epoch, which suggests that the model has the potential to learn from the complex data. Through the extensive utilisation of clinical data with imaging, we designed reliable diagnostic tools that have more chances to prognosticate accurately. Moreover, it has made a significant contribution to the validation of deep learning in interpreting medical data. Most importantly, it will also give a kind of approach to future work to further extend these methods for other cancers.