Hybrid Deep Learning Approach for Accurate Tumor Detection in Medical Imaging Data.

The automated extraction of critical information from electronic medical records, such as oncological medical events, has become increasingly important with the widespread use of electronic health records. However, extracting tumor-related medical events can be challenging due to their unique characteristics. To address this difficulty, we propose a novel approach that utilizes Generative Adversarial Networks (GANs) for data augmentation and pseudo-data generation algorithms to improve the model's transfer learning skills for various tumor-related medical events.

A Deep Learning-Based Framework for Uncertainty Quantification in Medical Imaging Using the DropWeak Technique: An Empirical Study with Baresnet.

Lung cancer is a leading cause of cancer-related deaths globally. Early detection is crucial for improving patient survival rates. Deep learning (DL) has shown promise in the medical field, but its accuracy must be evaluated, particularly in the context of lung cancer classification.

Machine learning based efficient prediction of positive cases of waterborne diseases.

Background: Water quality has been compromised and endangered by different contaminants due to Pakistan's rapid population development, which has resulted in a dramatic rise in waterborne infections and afflicted many regions of Pakistan. Because of this, modeling and predicting waterborne diseases has become a hot topic for researchers and is very important for controlling waterborne disease pollution.

Methods: In our study, first, we collected typhoid and malaria patient data for the years 2017-2020 from Ayub Medical Hospital.

SegChaNet: A Novel Model for Lung Cancer Segmentation in CT Scans.

Accurate lung tumor identification is crucial for radiation treatment planning. Due to the low contrast of the lung tumor in computed tomography (CT) images, segmentation of the tumor in CT images is challenging. This paper effectively integrates the U-Net with the channel attention module (CAM) to segment the malignant lung area from the surrounding chest region.

Dog ear graft for closure of the large skin defects in meningomyelocele surgery.

The closure of large skin defects is one of the challenging problems in plastic surgery and neurosurgery. Different reconstructive surgical techniques can be used for closing large skin defects. We report a technique for closing the large skin defects after the surgical repair of meningomyeloceles.

Comparison of free anterolateral thigh flaps and free muscle-musculocutaneous flaps in soft tissue reconstruction of lower extremity.

Background: The objective of this study was to compare the free muscle-musculocutaneous flaps and free perforator skin flaps used for soft tissue reconstruction of the lower extremities.

Methods: Fifty-three patients whose skin and soft tissue of the lower extremities had been reconstructed were divided into two groups: a perforator flap group, reconstructed using anterolateral thigh (ALT) free flap (23 cases), and a muscle-musculocutaneous flap group, in whom latissimus dorsi and rectus abdominus muscle-musculocutaneous free flaps were used (30 cases). Postoperative complications, long-term results, and donor site morbidities were studied in the two groups.

Three-dimensional multislice spiral computed tomographic angiography: a potentially useful tool for safer free tissue transfer to complicated regions.

Three-dimensional multislice spiral computed tomographic angiography (3D-MSCTA) is a minimally invasive method of vascular mapping. The aim of this study was to evaluate the clinical usefulness of this imaging technique in delineating the recipient vessels for safer free tissue transfer to complicated regions. 3D-MSCTA was performed preoperatively in 26 patients scheduled for free tissue transfer, in whom the availability of the recipient vessels were considered to be uncertain, and 23 of these were operated on.