## 1 Introduction  
Primary hyperparathyroidism (pHPT) is a common calcium metabolism disorder mainly affecting middle-aged females. It usually presents as a sporadic disease, while hereditary forms are much less common [1]. A hyper-functioning solitary parathyroid adenoma accounts for 80-85% of pHPT, while multiple abnormally functioning parathyroid glands (multiglandular disease) are responsible for the rest of the cases [2]. The diagnosis of pHPT is established biochemically by documenting increased serum calcium and parathyroid hormone levels and excluding causes of secondary HPT [3]. Despite using calcimimetic drugs to lower serum calcium and parathyroid hormone levels, the surgical excision of abnormal parathyroid glands (PGs) is currently the irreplaceable remedy for HPT [4]. Severe secondary HPT is caused primarily by end-stage renal failure. In this situation, all PGs are enlarged, each to a different degree. Pre-operative localisation of abnormal PGs is highly desired to assist surgery [5; 6]. Neck ultrasound, parathyroid scintigraphy, dynamic contrast-enhanced computerised tomography (CT), 4-D CT, and magnetic resonance imaging (MRI) are the imaging modalities for this task [7]. Employing neck ultrasound and scintigraphy as the first diagnostic approach is common. The other imaging methods are usually reserved for negative or ambiguous results [8]. Parathyroid scintigraphy is performed with the intravenous injection of the radioactive tracer \({}^{99\text{m}}\)Tc-Sestamibi (MIBI) [9]. Two scintigraphic techniques are usually employed, dual-phase and thyroid subtraction [10]. The dual-phase technique includes acquiring early (10 min post-MIBI administration) and late (2 h post-injection) images of the neck and the mediastum. MIBI uptake by the thyroid gland challenges the identification of an underlying parathyroid adenoma in early images. However, prolonged tracer retention by most abnormal PGs facilitates their detection in late images because MIBI clears more rapidly from the normal thyroid [11]. Nevertheless, quick tracer washout from some PGs is a usual cause of false negative findings [11]. To address this issue, doctors use the thyroid subtraction technique, which involves the administration of a second radioactive tracer (\({}^{123}\)I or \({}^{99\text{m}}\)Tc-pertechnate) to depict the thyroid gland [12]. The thyroid image is digitally removed from early MIBI images. This helps avoid false-positive results caused by MIBI uptake in thyroid nodules. The techniques can be used separately or together. Single-photon emission computerised tomography (SPECT) or SPECT/CT imaging is also employed to enhance sensitivity and obtain precise location information in three-dimensional space [9]. Medical experts examine all the produced images. However, the findings are often not prominent or well visualised. As a result, composing the diagnostic report is a time-consuming procedure. Recent progress in artificial intelligence (AI), and more specifically in the subset entitled deep learning (DL) [13], has demonstrated pioneering methods for classifying and identifying findings of medical importance in medical images of various modalities. However, due to the absence of human assistance and supervision, DL suffers from the issue of explainability [14; 15; 16]. The decisions of the algorithms are neither transparent nor interpretable. The latter catalysed the research for explainable methods that combine the capabilities of deep models with the transparency of more traditional approaches. Recently, post hoc explainability algorithms have been proposed to improve the explainability of deep models [14; 17; 18]. The most intuitive paradigm is that of the gradient-weighted class activation mapping (Grad-CAM) algorithm [17]. Moreover, the local interpretable model-agnostic explanations (LIME) [19] algorithm has recently gained attention as well. The present study proposes a DL approach for identifying abnormal PGs in scintigraphic images.