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. Our approach involves a two-stage pre-processing and model training process, where the data is cleansed, normalized, and augmented using pseudo-data. We evaluate our approach using the i2b2/UTHealth 2010 dataset and observe promising results in extracting primary tumor site size, tumor size, and metastatic site information. The proposed method has significant implications for healthcare and medical research as it can extract vital information from electronic medical records for oncological 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. In this study, we conducted uncertainty analysis on various frequently used DL architectures, including Baresnet, to assess the uncertainties in the classification results. This study focuses on the use of deep learning for the classification of lung cancer, which is a critical aspect of improving patient survival rates. The study evaluates the accuracy of various deep learning architectures, including Baresnet, and incorporates uncertainty quantification to assess the level of uncertainty in the classification results. The study presents a novel automatic tumor classification system for lung cancer based on CT images, which achieves a classification accuracy of 97.19% with an uncertainty quantification. The results demonstrate the potential of deep learning in lung cancer classification and highlight the importance of uncertainty quantification in improving the accuracy of classification results. This study's novelty lies in the incorporation of uncertainty quantification in deep learning for lung cancer classification, which can lead to more reliable and accurate diagnoses in clinical settings.

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. The collected data set has seven important input features. In the current study, different ML models were first trained and tested on the current study dataset using the tenfold cross-validation method. Second, we investigated the importance of input features in waterborne disease-positive case detection. The experiment results showed that Random Forest correctly predicted malaria-positive cases 60% of the time and typhoid-positive cases 77% of the time, which is better than other machine-learning models. In this research, we have also investigated the input features that are more important in the prediction and will help analyze positive cases of waterborne disease. The random forest feature selection technique has been used, and experimental results have shown that age, history, and test results play an important role in predicting waterborne disease-positive cases. In the end, we concluded that this interesting study could help health departments in different areas reduce the number of people who get sick from the water.

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. The SegChaNet method encodes CT slices of the input lung into feature maps utilizing the trail of encoders. Finally, we explicitly developed a multiscale, dense-feature extraction module to extract multiscale features from the collection of encoded feature maps. We have identified the segmentation map of the lungs by employing the decoders and compared SegChaNet with the state-of-the-art. The model has learned the dense-feature extraction in lung abnormalities, while iterative downsampling followed by iterative upsampling causes the network to remain invariant to the size of the dense abnormality. Experimental results show that the proposed method is accurate and efficient and directly provides explicit lung regions in complex circumstances without postprocessing.

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. RESULTS: Complete flap survival was 78.3% with four total and one partial flap loss in the ALT group and 90.0% with one total and two partial failure in the muscle-musculocutaneous flap group. Muscle-musculocutaneous flaps were the flaps of choice in Gustillo grade IIIB-C injuries and for reconstruction of more proximal localizations. ALT was preferred in relatively younger patients and was typically used for coverage of the distally localized defects. Flap complication rate was significantly higher in the ALT group, but the overall complication rate was similar between the groups. CONCLUSION: ALT perforator flap is a precious option for lower extremity soft tissue reconstruction with minimal donor site morbidity. Nevertheless, the beginners should be attentive to an increased rate of flap complications with the ALT flap and free axial muscle-musculocutaneous flaps would still be the tissue of choice for coverage of leg defects for a surgeon before gaining enough experience with perforator flap dissection.

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. Radiographic and operative findings regarding the availability of the recipient vessels for anastomosis were correlated in 21 of these 23 patients. 3D-MSCTA yielded two false-positive results; anastomosis was not possible because of widespread atherosclerotic plaques and poor flow observed in the recipient arteries despite the good caliber observed in 3D-MSCTA images. 3D-MSCTA provides a noninvasive means of preoperatively assessing recipient site vessels for anatomic variations and suitability before free tissue transfer and enables the surgeon to establish an appropriate treatment plan. But it is not 100% reliable yet and the possibility of false-positive results should be kept in mind, especially inthe patients with peripheral vascular disease. 3D-MSCTA has the potential to replace digital subtraction angiography for planning of microvascular reconstructions and newer devices with higher resolutions will probably increase the reliability of this technique. (c) 2009 Wiley-Liss, Inc. Microsurgery, 2009.