Despite remarkable advances in medical large language models (LLMs), their deployment in real clinical settings remains impractical due to prohibitive computational requirements and privacy regulations that restrict cloud-based solutions. Small LLMs (sLLMs) offer a promising alternative for on-premise deployment, yet they require domain-specific fine-tuning that still exceeds the hardware capacity of most healthcare institutions. Furthermore, the impact of multilingual data composition on medical sLLM performance remains poorly understood. We present a resource-efficient fine-tuning pipeline that integrates Quantized Low-Rank Adaptation (QLoRA), Fully Sharded Data Parallelism (FSDP), and Sequence Packing, validated across two model scales: MedGemma 4B for efficiency analysis and LLaMA 3.3 70B for data balance experiments. Our approach achieves 58.3% reduction in video random access memory (VRAM) usage (from 48 GB to 20 GB) and 5× training speedup on MedGemma 4B using NVIDIA L40s GPUs. Critically, experiments on LLaMA 3.3 70B reveal that English-heavy data mixing (10:3 ratio) degrades Korean medical law performance by 1.23 percentage points while providing only marginal English gains (+1.49 pp), demonstrating catastrophic forgetting in multilingual medical fine-tuning. Our work provides three contributions: (1) a practical fine-tuning pipeline operable within 20 GB VRAM, (2) empirical evidence that data balance—not volume—determines multilingual medical QA performance, and (3) actionable guidelines for deploying medical sLLMs in non-English clinical environments.

It has been demonstrated that fuzzy systems are beneficial for classification and regression. However, they have been mainly utilized in controlled settings. An image clustering technique essential for content-based picture retrieval in big image datasets is developed using the contents of color, texture and shape. Currently, it is challenging to label a huge number of photos. The issue of unlabeled data has been addressed. Unsupervised learning is used. K-means is the most often used unsupervised learning algorithm. In comparison to fuzzy c-means clustering, K-means clustering has lower-dimensional space resilience and initialization resistance. The dominating triple HSV space was shown to be a perceptual color space made of three modules, S (saturation), H (hue) and V (value), referring to color qualities that are significantly connected to how human eyes perceive colors. A deep learning technique for segmentation (RBNN) is built on the Gaussian function, fuzzy adaptive learning control network (FALCN), clustering and the radial basis neural network. The segmented image and critical information are fed into a radial basis neural network classifier. The suggested fuzzy adaptive learning control network (FALCN) fuzzy system, also known as the unsupervised fuzzy neural network, is very good at clustering images and can extract image properties. When a conventional fuzzy network system receives a noisy input, the number of output neurons grows needlessly. Finally, random convolutional weights extract features from data without labels. Furthermore, the state-of-the-art uniting the proposed FALCN with the RBNN classifier, the proposed descriptor also achieves comparable performance, such as improved accuracy is 96.547 and reduced mean squared error of 36.028 values for the JAFE, ORL, and UMIT datasets.