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This working note documents the participation of CS_Morgan in the ImageCLEFmedical 2024 Caption subtasks, focusing on Caption Prediction and Concept Detection challenges. The primary objectives included training, validating, and testing multimodal Artificial Intelligence (AI) models intended to automate the process of generating captions and identifying multi-concepts of radiology images. The dataset used is a subset of the Radiology Objects in COntext version 2 (ROCOv2) dataset and contains image-caption pairs and corresponding Unified Medical Language System (UMLS) concepts. To address the caption prediction challenge, different variants of the Large Language and Vision Assistant (LLaVA) models were experimented with, tailoring them for the medical domain. Additionally, a lightweight Large Multimodal Model (LMM), and MoonDream2, a small Vision Language Model (VLM), were explored. The former is the instruct variant of the Image-aware Decoder Enhanced à la Flamingo with Interleaved Cross-attentionS (IDEFICS) 9B obtained through quantization. Besides LMMs, conventional encoder-decoder models like Vision Generative Pre-trained Transformer 2 (visionGPT2) and Convolutional Neural Network-Transformer (CNN-Transformer) architectures were considered. Consequently, this enabled 10 submissions for the caption prediction task, with the first submission of LLaVA 1.6 on the Mistral 7B weights securing the 2nd position among the participants. This model was adapted using 40.1M parameters and achieved the best performance on the test data across the performance metrics of BERTScore (0.628059), ROUGE (0.250801), BLEU-1 (0.209298), BLEURT (0.317385), METEOR (0.092682), CIDEr (0.245029), and RefCLIPScore (0.815534). For the concept detection task, our single submission based on the ConvMixer architecture—a hybrid approach leveraging CNN and Transformer advantages—ranked 9th with an F1-score of 0.107645. Overall, the evaluations on the test data for the caption prediction task submissions suggest that LMMs, quantized LMMs, and small VLMs, when adapted and selectively fine-tuned using fewer parameters, have ample potential for understanding medical concepts present in images.