Orthopantomograms (OPGs) hold fundamental significance in the practice of diagnostic dentistry. They provide a panoramic image of the maxillofacial region. Deep learning can automate and enhance the analysis of OPGs, which assists clinicians in detecting dental pathologies more efficiently and consistently by utilizing automated systems. In this paper, we propose a solution for multi-class, image-level classification of common dental pathologies using OPG X-rays. A feature extractor with a pretrained ResNet50 architecture was employed, augmented with a Convolutional Block Attention Module (CBAM) to capture significant pathological attention. The dataset was balanced using augmentation methods and contained six classes of dental conditions: BDC-BDR, Fractured Teeth, Impacted Teeth, Caries, Healthy Teeth, and Infection. We present the model architecture, training strategy, and the experimental details for our evaluation. Initial findings indicate the promise of this method, as well as areas for enhancement, such as broadening the approach to multi-label classification and exploring multi-backbone alternatives.