# Empowering Healthcare: TinyML for Precise Lung Disease Classification
## 2 Related Works  
Numerous research papers have examined the application of DL to identify patterns and distinguish among various lung conditions by using raw respiratory sound data. The authors of [24] developed a framework for classifying various respiratory diseases by using lung sound recordings. They employed a CNN with Mel-Frequency Cepstral Coefficients (MFCC) for feature extraction. The proposed model achieved a classification accuracy of 95.7%, effectively distinguishing among different respiratory diseases, such as asthma, COPD, URTI, LRTI, and bronchiectasis, and a class representing healthy people. In [25], the authors developed a framework for classifying lung sounds, addressing the challenge of noise interference from the heart and lung sounds. By utilizing two public datasets with 280 lung sounds of varying durations and sampling rates, the study preprocessed signals for uniformity and employed Mel-Frequency Cepstral Coefficients (MFCCs) [26] and Short-Time Fourier Transform (STFT) [27] for feature extraction. It tested several models, achieving the highest accuracy with an STFT+MFCC-ANN combination, demonstrating promising results for automatic respiratory diagnosis with high precision and recall rates. In the paper [5], the authors evaluated the efficacy of various deep learning models for diagnosing respiratory pathologies by using lung auscultation sounds. The study compared three deep learning models across both non-augmented and augmented datasets, revealing that the CNN-LSTM model outperformed others with high accuracy rates in all scenarios. Augmentation significantly enhances model performance, with the CNN-LSTM hybrid showing particular strength by combining the CNN's feature extraction capabilities with LSTM's [28] classification efficiency. In [29], the authors explored the effectiveness of Mel Frequency Cepstral Coefficients (MFCCs) in classifying cough sounds for diagnosing five respiratory diseases, such as asthma and chronic obstructive pulmonary diseases (COPDs). The study employed a unique ensemble of recurrent neural network models with LSTM cells and tested various meta-classifiers, achieving over 87% accuracy. This approach underscores MFCCs' potential as standalone features for cough signal classification and suggests future directions, including further disease characteristic diagnosis and COVID-19 cough classification. In [30], the authors tackled the challenge of detecting respiratory pathologies from sounds, using the ICBHI Benchmark dataset. Given the dataset's imbalance, the study employed a Variational Convolutional Autoencoder for data augmentation, alongside traditional oversampling techniques. A CNN was employed for classification into healthy, chronic, and non-chronic categories, achieving an F-score of 0.993 in the three-label classification. For the six-class classification, which included RTI, COPD, Bronchiectasis, Pneumonia, and Bronchiolitis, the CNN achieved an F-score of 0.99. In [31], the authors developed a non-invasive method for classifying respiratory sounds by using an electronic stethoscope and audio recording software. By using MFCC with SVM and spectrogram images with CNN, they benchmarked the CNN's performance against the SVM method across various sound classifications. The CNN and SVM both reached 86% in distinguishing healthy versus pathological sounds; for rale, rhonchus, and normal sounds, the CNN achieved 76% and SVM 75%; in singular-sound-type classification, both achieved 80%. These results underline the effectiveness of CNNs and SVM in respiratory sound analysis. In [32], the authors developed a system for diagnosing asthma using deep learning by analyzing respiratory sounds from asthmatic and non-asthmatic individuals. They developed a web interface and a mobile app for real-time prediction, aiding doctors in performing accurate diagnoses.