## 1 Introduction  
The kidney, an abdominal organ that filter waste and excess water from the blood, can developed abnormalities [1] that have a significant impact on health and affect over 10% of the worldwide population [2]. The exact causes and mechanisms of kidney abnormalities have yet to be fully understood [3], but research in recent years has identified a variety of genetic, environmental, and lifestyle factors that may contribute to their development [4]. The severe symptoms of kidney abnormalities include a pain in the side or back; blood in the urine; fatigue; and swelling in the legs and feet [5]. The most common kidney abnormalities that impair kidney function are the development of cysts, nephidithiasis (kidney stones), and renal cell carcinoma (kidney tumor) [6]. Kidney stones are hard crystalline mineralized material that varies in size and is formed in the kidney or urinary tract. The complex phenotype of kidney stone disease is caused by the interplay of several genes, in conjunction with dietary and environmental variables [7]. Another urinary problem, a cyst, is a fluid-filled sac in the kidneys [8], whereas tumors are abnormal growths with symptoms such as abdominal pain, blood in the urine, and unexplained weight loss [9]. In the early stages, patients may not show any symptoms or signs of kidney abnormalities, as they are initially benign. However, if untreated, they can develop further and even become malignant. For early detection and treatment, pathology tests are frequently used, with imaging techniques including X-rays, computer tomography (CT), B-ultrasound devices, and magnetic resonance imaging (MRI) [10]. Ultrasound devices that use high-frequency sound waves and MRIs may not be suitable for patients with medical implants or conditions. CT scans are generally faster and provide detailed images of dense tissues, such as bones in the spine and skull [11]. Despite its limitations, such as non-standardized image acquisition [12]; the improper labeling of data collected; and regulatory barriers [13], deep-learning (DL) research has show a significant positive impact in radiology applications, such as reducing the workload of radiologists [14]; increasing medical access in developing countries and rural areas [15]; and improving diagnostic accuracy [16]. DL models have been applied for a wide range of concerns, such as COVID-19 [17], brain tumors [18], radiation therapy [19], blood clotting [20], heart diseases [21], liver cancer [22], fin-tech [23], intrusion detection [24], vibration signals [25], organic pollutant classification [26], agriculture [27], and steganography [28]. Recent research has shown that DL models can be used successfully for diagnosing kidney abnormalities [29; 30; 31; 32; 33]. However, in diagnosing kidney abnormalities, the lightweight models [33] have achieved low accuracy, as compared to the more complex models [29; 30; 31; 32]. Therefore, a lightweight customized DL model could be considered as an alternative approach to achieve higher accuracy. Meanwhile, explainable artificial intelligence (XAI) [34] is a pragmatic tool that accelerates the creation of predictive models with domain knowledge and increases the transparency of automatically generated prediction models in the medical sector [35; 36], which has assisted in providing results that are understandable to humans [37]. We noted that most of the recent research defined the detection of kidney problems in terms of individual categories, including stones [38], tumors [39], and cysts [40]. However, the proposed study classified all three abnormalities using a single model, and it provided the following **contributions**. 1. For diagnosing three different kidney abnormalities, a fully automated lightweight DL architecture was proposed. The model's capability to identify stones, cysts, and tumors was improved by utilizing a well-designed, customized convolutional neural network (CNN). 2.