Enhancing Multi-Class Disease Classification: Neoplasms, Cardiovascular, Nervous System, and Digestive Disorders Using Advanced LLMs

In this research, we explored the improvement in terms of multi-class disease classification via pre-trained language models over Medical-Abstracts-TC-Corpus that spans five medical conditions. We excluded non-cancer conditions and examined four specific diseases. We assessed four LLMs, BioBERT, XLNet, and BERT, as well as a novel base model (Last-BERT). BioBERT, which was pre-trained on medical data, demonstrated superior performance in medical text classification (97% accuracy). Surprisingly, XLNet followed closely (96% accuracy), demonstrating its generalizability across domains even though it was not pre-trained on medical data. LastBERT, a custom model based on the lighter version of BERT, also proved competitive with 87.10% accuracy (just under BERT's 89.33%). Our findings confirm the importance of specialized models such as BioBERT and also support impressions around more general solutions like XLNet and well-tuned transformer architectures with fewer parameters (in this case, LastBERT) in medical domain tasks.

Chronic Obstructive Pulmonary Disease Prediction Using Deep Convolutional Network

AI and deep learning are two recent innovations that have made a big difference in helping to solve problems in the clinical space. Using clinical imaging and sound examination, they also work on improving their vision so that they can spot diseases early and correctly. Because there aren't enough trained HR, clinical professionals are asking for help with innovation because it helps them adapt to more patients. Aside from serious health problems like cancer and diabetes, the effects of respiratory infections are also slowly getting worse and becoming dangerous for society. Respiratory diseases need to be found early and treated quickly, so listening to the sounds of the lungs is proving to be a very helpful tool along with chest X-rays. The presented research hopes to use deep learning ideas based on Convolutional Brain Organization to help clinical specialists by giving a detailed and thorough analysis of clinical respiratory sound data for Ongoing Obstructive Pneumonic identification. We used MFCC, Mel-Spectrogram, Chroma, Chroma (Steady Q), and Chroma CENS from the Librosa AI library in the tests we ran. The new system could also figure out how serious the infection was, whether it was mild, moderate, or severe. The test results agree with the outcome of the deep learning approach that was proposed. The accuracy of the framework arrangement has been raised to a score of 96% on the ICBHI. Also, in the led tests, we used K-Crisp Cross-Approval with ten parts to make the presentation of the new deep learning approach easier to understand. With a 96 percent accuracy rate, the suggested network is better than the rest. If you don't use cross-validation, the model is 90% accurate.