Cancer remains a critical global health challenge, with the World Health Organization (WHO) projecting 35 million new cases by 2050, necessitating advanced diagnostic tools such as whole-body FDG-PET imaging, which detects metabolic activity in pathologies. While 3D PET scans are powerful, their computational demands become excessive when dealing with deep learning, motivating the use of efficient 2D representations, moreover, this representation would assist radiologists in analyzing exams, since there are too few specialized professionals to interpret all the scans. This work proposes a Swin Transformer-based method to classify 2D Maximum Intensity Projection (MIP) images from FDG-PET scans into binary categories (cancerous vs. healthy), addressing challenges of multi-cancer detection (melanoma, lymphoma, lung cancer) and variability in image coverage. The approach achieved results of 82.08% \(\,\pm \,\) 4.3% accuracy, 82.43% \(\,\pm \,\) 2.9% F1-Score, 84.31% \(\,\pm \,\) 4.4% recall, and 81.35% \(\,\pm \,\) 7.1% precision. These results demonstrate the viability of the proposed approach, which combines the computational efficiency of 2D representations with the discriminative capability of the Swin Transformer architecture for medical image analysis.