Melanoma is the deadliest skin cancer, causing over 90% of skin tumor deaths. Early detection ensures survival above 95%, but below 15% in late stages. We propose the HEVA architecture, which integrates ensemble deep learning with ABCD descriptors and a SegFormer-B5 model, for accurate lesion masks. HEVA achieved 93.2% accuracy and 84.2% F1-score on HAM-10000, and 87.7% accuracy with 82.01% macro F1 on ISIC 2018 (1,512 images). SegFormer achieved 82.6% mean IoU and 91.2% accuracy in segmentation.
Coffee is one of the main agricultural products exported by Brazil, with Minas Gerais standing out as the largest producer and responsible for a significant portion of the country's production. Due to its importance, this work investigates various Convolutional Neural Networks (CNNs) for mapping coffee plantation areas in the municipality of Muzambinho, MG. Using satellite images, we evaluated four semantic segmentation network architectures: FCN, MA-NET, SegNet, and DeepLabV3. Experiments using a dataset with 100 satellite images demonstrated that the FCN and MA-NET networks achieve better performance in identifying crops than SegNet and DeepLabV3. The latter, however, presented generalization challenges in the test set, indicating sensitivity to variations in lighting and shading. The study highlights the potential of artificial intelligence combined with remote sensing to improve agricultural management, and the need for more robust and diversified datasets to optimize the generalization and robustness of the models in real conditions.
Accurate segmentation of spinal structures in magnetic resonance imaging (MRI) is an important step for supporting the diagnosis of several diseases and related conditions. In this work, we present a systematic evaluation of three deep learning architectures, U-Net, Feature Pyramid Network (FPN), and SegFormer, combined with ResNet-50 and EfficientNet-B2 encoders for the semantic segmentation of the cervical spine (C1–C7) in MRI volumes from the VerSe 2020 dataset. The proposed pipeline includes dataset preparation, model training using Dice loss, Adam optimizer, and early stop strategy, and evaluation with standard metrics such as Intersection over Union (IoU), F1-Score, and accuracy. Results show that FPN with ResNet-50 achieved the best overall performance, reaching an IoU of 0.6696 and an F1-score of 0.8021, while EfficientNet-B2 provided more consistent results across different architectures. Data augmentation showed limited impact, with gains restricted to a few specific configurations. Qualitative analysis through 3D surface reconstruction further highlighted the limitations of 2D slice-based segmentation, particularly at the extremities of the vertebrae, suggesting the need for volumetric approaches. The contributions of this work include the development of a reproducible pipeline for spinal segmentation, a comparative evaluation of convolutional and Transformer-based models. Qualitative analysis suggests potential benefits in exploring 3D approaches in future work.
Automated quality control in industrial production has the potential to reduce errors and provide real-time information. However, the inspection of secondary packaging, such as counting boxes in crates, still represents a challenge, since manual methods are slow and error-prone, while automatic methods are limited by the scarcity of domain-specific datasets and the high cost of annotation. This paper proposes an efficient and low-cost two-stage object detection workflow for automatic box counting. The central novelty lies in the integration of the Segment Anything Model (SAM) to accelerate the creation of a high-quality dataset from production line videos, making model specialization economically feasible. Initially, a generalist model is trained on heterogeneous datasets to capture general visual features. Then, the model is fine-tuned with the domain-specific dataset of approximately 750 images. Experiments with YOLOv11x and Faster R-CNN achieved mAP@0.5 above 98%, with YOLOv11x showing higher accuracy and faster inference. These results demonstrate the efficiency of the proposed approach, establishing it as a replicable and low-cost solution for monitoring secondary packaging in industrial environments.
Air writing is a form of human–computer interaction that enables users to write in the air through different technological approaches, such as radio waves, wearable sensors, dedicated devices, and computer vision. In this work, we present a hybrid solution that combines the use of a luminous-tip pen with computer vision techniques to support pen detection and the air-writing process. The pen was designed and fabricated with a 3D printer and integrates an ESP32 microcontroller board, which provides Bluetooth connectivity for real-time communication with the computer. In addition to tracking movements for writing, the device can transmit specific commands to the system, such as starting or stopping the writing process and changing colors. This approach bridges the gap between wearable devices and vision-based methods, offering a practical and accessible alternative for air-writing applications. The system integrates vision–language models capable of recognizing both words and images. To determine which models were best suited for our system, we evaluated the performance of several candidates. Five participants contributed to the creation of the dataset. For drawing recognition, each participant produced images in the following classes: Tree, Moon, Cat, Heart, and Pen. For word recognition, the dataset included the following eight words: Purple, Window, Jungle, Pillow, Team, Doctor, Words, and Science. The results showed that the best-performing models achieved 76.00% accuracy with Kosmos-2 for image detection and 97.39% accuracy with Gemini 2.5 Flash for word detection, demonstrating satisfactory outcomes for the system implementation. The proposed system demonstrates the potential of combining electronic devices and advanced machine learning techniques for air-writing, with applications in education and assistive technologies.
