# Empowering Healthcare: TinyML for Precise Lung Disease Classification
## 1 Introduction  
According to the World Health Organization (WHO), lung diseases are among the leading causes of mortality worldwide, resulting in the deaths of millions of people each year [1]. Respiratory diseases are often detected late, making treatment less effective [2]. Various clinical approaches have been developed to diagnose and assess lung health issues, including computed tomographic scans, chest X-rays, and pulmonary function tests. However, these techniques are restricted to specialized medical facilities due to their complexity, high cost, and time-consuming nature [3]. Additionally, medical professionals in hospitals are often overworked, which increases the likelihood of errors and patient waiting times [3]. Therefore, it becomes apparent that a different approach is needed to better assist practitioners in making an initial diagnosis. In contrast, the stethoscope is used as a non-invasive and patient-friendly tool for diagnosing respiratory conditions through lung ausculation [4]. This procedure involves listening to the sounds produced by air moving in and out of the lungs. During lung auscultation, experts are able to identify various abnormal respiratory sounds, like wheezing and crackling [4]. These sounds serve as indicators of possible respiratory conditions for the patient. However, traditional stethoscopes come with several associated challenges. Firstly, their effectiveness relies heavily on the physician's expertise and judgment, introducing potential for diagnostic errors [5]. Secondly, they lack a recording feature, preventing other medical personnel from analyzing the sounds heard during consultations [6]. Thirdly, they are not equipped with noise-canceling capabilities, making it difficult to hear lung sounds in noisy environments such as emergency rooms or busy clinics [7]. The digital stethoscope has introduced a new approach to auscultation, benefiting research, education, and clinical practice [8]. It digitizes lung sounds, allowing for recording and playback, which reduces reliance on a single physician's judgment and enables collaboration with other medical professionals [8]. It incorporates digital filters to eliminate noise and isolate the relevant acoustic signals within specific frequency bands [8]. This enhances diagnostic accuracy and improves clinical decision making. It also allows for the visualization and retrospective analysis of lung sounds. The integration of wireless transmission capabilities, such as Bluetooth or WiFi, with the digital stethoscope will facilitate remote diagnosis, greatly enhancing convenience and application in a variety of medical contexts [8]. In recent years, there has been growing interest in the automated analysis of lung sounds. By using machine learning, particularly Deep Learning (DL) techniques, the experience, quality of diagnosis, and care for both patients and healthcare professionals have significantly improved [3; 8]. The utilization of DL algorithms to examine lung sound patterns captured by digital stethoscopes represents a promising approach for the early and precise detection of disease [9]. Moreover, these technologies aim not only to reduce dependency on specialist facilities but also to overcome the limitations of traditional stethoscopes, making diagnosis more accurate by removing human error [3]. However, coupling digital stethoscopes with DL presents certain limitations. DL algorithms require significant computational resources, posing challenges in resource-constrained environments [10; 11]. Latency in cloud-based solutions can impact real-time analysis, particularly in areas with insufficient internet bandwidth for large data transmission [10; 11; 12], and privacy concerns arise when transmitting sensitive health data over the internet [10; 11]. To address these limitations, Tiny Machine Learning (TinyML) [13] offers a compelling solution by enabling efficient ML codes to run on small, energy-efficient devices.