In modern healthcare, radiology imaging has emerged as a primary tool for the early detection of various diseases (Beets-Tan et al., 2020). Multiple modalities of radiological imaging are employed, including X-ray, Computed Tomography (CT), Magnetic-Resonance Imaging (MRI), Positron Emission Tomography (PET), and Ultrasound (US). Among these, chest radiography is particularly notable for its widespread use in achieving precise and rapid diagnoses (Abhisheka et al., 2023; Hussain et al., 2022; Philip et al., 2023). Radiologists, in the context of chest diseases, predominantly rely on X-rays and CT scans (Hall and Giaccia, 2006).

In most radiological imaging chest diseases, Pneumonia is an acute infection affecting the alveoli and distal airways of one or both lungs. It causes narrowing or closing of the airways due to inflammatory cells and fluid deposition in the alveolar sack of the lungs. It is a common and potentially lethal illness, affecting more frequently susceptible individuals, especially children of < 5 years of age, accounting for 15 % of all deaths caused in this year's groups. Babies born too early (prematurely) face higher health risks because their organs are still developing. The younger the baby is, the higher the chance of getting lung infection (Prodanovic et al., 2024). The infectious agents can be viruses, bacteria, or fungi with a great geographical prevalence and outcome variations, among developed and low-and-middle-income countries. Yearly over millions of people are being infected with this virus. Early diagnosis is crucial for the successful treatment process. Generally, the disease can be diagnosed from chest X-ray images, but it is challenging due to unclear appearances and confusion with other diseases. There is an increasing effort to reach the poor community, in term of helping strategies recognizing and managing pneumonia (Rudan et al., 2004; Torres et al., 2021; Bhutta, 2007; Nelson et al., 1995; UNICEF, 2022; Franquet, 2001). In Africa, a significant shortage of healthcare workers poses a critical challenge, with an estimated deficit of 2.4 million doctors and nurses. Numerous studies have highlighted the severe scarcity of medical professionals and other healthcare resources across the continent (Narasimhan et al., 2004; Naicker et al., 2009). Due to critical health issues, the early detection of this disease, especially in neonates, is very important as it interferes with brain growth development and leads to other visual and hearing impairments including heart diseases (Maeda et al., 2023; Yildirim and Canayaz, 2023; Prodanovic et al., 2024).

Considering AI as a promising tool in medical imaging, AI can improve pediatric pneumonia diagnosis in the early stages (Yoon and Kang, 2024; Bal et al., 2024). In this scenario, specific kinds of challenges arise when working with pediatric chest X-ray datasets. Firstly, the available data scarcity, as compared to adult datasets, can hinder the development of robust trained AI models. Secondly, pediatric X-ray images reveal diverse anatomical development structure as compared to adults. Thirdly, it is also very challenging to acquire high quality images from uncooperative young ages patients and will result in various noise factors and artifacts caused by positioning and breathing patterns. Due to this, it will lead to more complicated analysis. Finally, ethical rules and regulations come into play even more critically when dealing with pediatric patients as compared to adults. Strict anonymization protocols and parental consent procedures are very important to ensure data collection and further utilization (Singh, 2024; Candemir and Antani, 2019; Ciet et al., 2024). Considering all these challenges, the collaborative effort of data collection and the development of robust AI powered diagnostic tools require standard image normalization techniques and strict anonymity rules and regulations for early diagnostic of pneumonia in pediatric patients.

Over the last 20 years, it is noted that chest infection has decreased. New conjugate vaccines for diagnosing Haemophilus influenzae type b and Streptococcus pneumonia helped to decrease in radiologic, clinical and immense pneumonia cases have reduce hospitalization (Le Roux and Zar, 2017). In 2011, the western pacific region, recorded that 61,900 yearly deaths are due to pneumonia in < 5-year-old children (Nguyen et al., 2017). According to world health organization (WHO), all these extraordinary reductions in deaths, pneumonia is one of biggest killer of children < 5 years old (Chavez et al., 2015).

Today, chest X-ray is used to show the presence of different lung diseases in humans. Experienced radiologists can assume the probability of chest disease in humans after checking the x ray images. Notably, in 2019, hospitals in Italy and the UK primarily employed radiography imaging to diagnose coronavirus patients (Fields et al., 2020). In some regions of the world, there is a limited amount of medical equipment and a shortage of doctors and experienced professionals who can effectively interpret chest X-rays. The most often experts of radiology examine the two-dimension (2D) chest x-ray images to clarify the pneumonia bacteria or virus instead of three-dimension (3D) chest structure (Mahomed et al., 2020).

The ease of obtaining radiographic images, however, also poses a significant challenge, as a single radiologist may need to evaluate as many as 100 images daily through radiography imaging [3]. Therefore, medical image analysis is a time taking process to reach chest disease diagnosis. In this scenario, information technology (IT) has a cardinal importance and is assisting the radiologists. It has also demonstrated itself as a backbone in other medical fields too. Further, health IT is administering the information of different kinds of disease using computational knowledge and its progress. The promising capability of health IT in decision making is a lot more as compared to a human. In this way, health IT can provide more better assistance in diagnosing various diseases to all over the world's medical community such as for acute coronary syndrome (D'Ascenzo et al., 2021), cardio-vascular risk (Navarini et al., 2020), and so on. Moreover, the most tremendous things in IT is the available data on the internet and that anyone can access at any time (Bohr and Memarzadeh, 2020). In parallel, advancements in artificial intelligence (AI) has led the medical field to consider AI as a promising tool for tackling complex biomarkers and supporting medical researchers in comprehending the progression of various diseases (Xing et al., 2020).

In this comprehensive review, we have examined and compared 20 computer-aided systems designed to assist clinicians in diagnosing pneumonia. Our exploration began with an in-depth analysis of publicly available datasets. We also provided detailed insights into various pre-processing techniques to aid researchers related to the available data. Furthermore, we conducted an extensive discussion on the various machine learning and deep learning algorithms that have been predominantly utilized in recent research efforts. Our aim is to highlight the best models for a reliable and versatile tool useful in the middle- and low-income communities.

The paper is divided into several sections. Section 2 focuses on the dataset and its modality. Section 3 describes the data preprocessing and balancing techniques, and Section 4 discusses the different performance measures used in the studies. Section 5 elaborates numerous techniques proposed in this domain and section 6 provides a detailed comparative analysis and discussion of all techniques used for chest diseases' classification. Section 7 discusses progress and challenges in research works related to chest diseases. Finally, Section 8 concludes the review work.

In contemporary times, a variety of datasets for lung diseases, complete with labels, have become accessible for training diverse machine learning and deep learning algorithms. These datasets have consistently served as standard references in numerous research endeavors, facilitating the comparison of results achieved through different techniques. Considering the wealth of available datasets, it is evident that they provide ample resources for training, validating, and testing machine learning and deep learning algorithms, including the creation of holdout datasets for evaluation. In our study, we focused on several benchmark datasets containing chest disease data and reviewed pneumonia experiments on these datasets, as mentioned below.

The purpose of preprocessing is to improve the quality of images by reducing variations and eliminating any discrepancies that may cause complications during the processing stage, thereby reducing the consumption of resources (Tripathy and Swarnkar, 2020). Preprocessing achieves three main objectives, namely noise removal, contrast enhancement, and brightness and color correction. To remove noise, filters such as mean and median filters, and Gaussian low-pass filtering are widely used. Morphological techniques are also used for image information enhancement purposes (Courtenay et al., 2020). Contrast stretching techniques and histogram equalization techniques have been widely used for contrast enrichment. For brightness, color correction, and color standardization techniques have been used also such as Gastal and Oliveira (2012).

Furthermore, standard x-ray images generally have a dimension of (3,000 x 2,000) pixels, which is quite large and contains unnecessary information. These large images require high storage space and powerful hardware for analysis. To save time and obtain better results, many publicly available datasets have resized their images from their original size. For example, the Chest X-ray 14 dataset has reduced the image sizes to 512 x 512 pixels, while the MIMIC-CXR-JPG and OCT-CXR datasets have image sizes of (2,048 x 2,048) and (1,024 x 1,024) pixels, respectively. In our studies, we have also emphasized the importance of a balanced experimental dataset for training, validation, test sets. An unbalanced data set can lead to biased classifications toward the majority image set. Therefore, data balancing techniques are used to prevent such biased classification results. Table 2 illustrates some traditional and deep learning data balancing techniques along with their descriptions.

The performance measures (Michelucci et al., 2021) for pneumonia detection and classification depend upon various factors. These factors are discussed below:

The following equations represent the performance metrics used to evaluate the robustness of the models: accuracy (1), sensitivity (2), specificity (3), and precision (4).

Over time, machine learning approaches, a general concept, have been shown Figure 2 to be implemented in numerous studies (Hameed et al., 2020; Javaid and Haleem, 2020), significantly assisting in the medical field. Even today, machine learning is useful in the medical field and helps researchers continue to address novel medical issues. A number of relevant studies on pneumonia have been published in the subsequent subsections based on CXR datasets. These used CXR datasets are frequently used in research as a standard dataset for comparison with the results of other studies. In our review, we adhered to the same framework.

In comparative analysis, we have evaluated the performance of various conventional machine learning and deep learning models, including customized CNN, transfer learning, and embedded models, utilizing different feature extraction techniques. For a more enhanced overview, different research works have been clustered into meaningful groups.

The performance metrics listed in this review are recommended methods for determining each model's performance. However, we have provided all the relevant measurements as shown in Table 3. To facilitate comparison, all the studies previously described were evaluated based on accuracy, with additional performance measures noted where available.

In the groups, ML group depicts that Oliveira et al. (2008) achieved the best 97% accuracy by employing the k-NN classifier on numerous extracted features. With respect to customized neural networks, Labhane et al. (2020) achieved ~97% accuracy, with the best results obtained with all the customized and pretrained models. Using the pretrained transfer learning technique, Rahman et al. (2020) and Hashmi et al. (2020) achieved 98% accuracy by employing numerous pretrained models by comparing among them. In the last group, Togaçar et al. (2020) achieved 99% accuracy by employing transfer learning and the ensemble feature technique. Overall, we found that Togaçar et al. (2020) performed very well, with 99% accuracy, by using transfer learning and the ensemble feature technique. This shows that transfer learning with an ensemble method is an efficient and effective technique for learning and representing better visual features of images.

As AI continues to advance over time, it has become increasingly convenient to develop improved algorithms, reducing the reliance on manually crafted features for radiographic images. Researchers have made significant strides in creating numerous algorithms designed to automatically extract features and employ machine/deep learning techniques. The primary benefit of these methods lies in their enhanced performance, particularly in the detection and classification of various chest diseases using deep learning algorithms. Many recent studies have harnessed convolutional neural networks (CNNs) to augment early-stage detection by learning intricate patterns, thereby helping doctors comprehend these complex situations.

However, a notable challenge is the lack of practical applicability, which hinders the widespread use of these highly efficient algorithms. There is a pressing need to bridge the gap between related research and wider public and medical communities by creating user-friendly interfaces on well-performing models. Moreover, there is a deficiency in training resources because of the limited tools available, leaving many users uncertain about how to effectively utilize these tools in middle- and low-income regions for early diagnosis and mitigation of chest and related diseases.

In addition, while the number of studies on the detection of pneumonia using machine learning/deep learning is growing, the number of works that focus on explainability is still relatively limited. This is a significant gap in the literature, as explainability is crucial for understanding how models make decisions and for building trust in their results.

The detection of lung diseases from medical images poses significant challenges for radiologists, even for experienced professionals, due to the intricacies of these diseases and the time-intensive nature of the diagnostic process. However, artificial intelligence (AI) has emerged as a promising solution for addressing these challenges. Artificial intelligence (AI), particularly through machine learning and deep learning techniques, has revolutionized the field of medical research, replaced traditional handcrafted methods and significantly improved diagnostic accuracy. In this review, we have presented a vast amount of research on chest disease classification using four different standard radio imaging datasets of lungs. Among the datasets considered, the CXR dataset is the most commonly used dataset among the reviewed works compared to the ChestX-ray14, JSRT, and MC datasets. For classification, different image preprocessing and feature extraction techniques have been employed to train different machines and deep learning algorithms. Overall, it has been found that transfer learning techniques with ensemble models/features result very well-compared to machine learning, custom-designed deep models, and transfer learning models. Additionally, our work highlights the importance of XAI, and its usage in limited works can unveil hidden reasoning. Ultimately, this comprehensive review will not only benefit researchers related to lung diseases but also increase the interest of researchers working on images, machine learning, machine vision, deep learning, and other related areas.