5 research outputs found
An overview of skin cancer classification based on deep learning
No full textيعد سرطان الجلد الجلدي من أخطر الأمراض في العالم. التصنيف الصحيح للآفات الجلدية في خطوة أولية يمكن أن يساعد في خلق حكم سريري من خلال توفير الحكم الأمثل للمرض، مما قد يزيد من احتمالات العلاج في وقت مبكر من انتشار السرطان. وفي الوقت نفسه، يعد التصنيف التلقائي لسرطان الجلد أمرًا صعبًا بسبب عدم التوازن في معظم صور سرطان الجلد المستخدمة في التدريب. في الآونة الأخيرة، تم استخدام عدة طرق تعتمد على التعلم العميق على نطاق واسع في تصنيف سرطان الجلد لحل مشاكل التصنيف وتحقيق نتائج مقبولة. ومع ذلك، فإن المراجعات التي تحتوي على الصعوبات الحدية المذكورة أعلاه في تصنيف سرطان الجلد لا تزال نادرة. ونتيجة لذلك، تقدم هذه الورقة ملخصًا لأحدث إجراءات التعلم العميق لتصنيف سرطان الجلد. تبدأ هذه الورقة بمناقشة أنواع سرطانات الجلد وتليها مجموعة بيانات عامة متاحة لسرطان الجلد. وبعد ذلك، تم تسليط الضوء على بعض نماذج CNN المدربة مسبقًا والمستخدمة في التصنيف. أخيرًا، قمنا بتلخيص بعض فرص الإصابة بسرطان الجلد مثل اختلال توازن البيانات ومحدوديتها، وشبكة الخصومة التوليدية، ومجموعات البيانات المختلفة، وزيادة البيانات.Skin melanoma is one of the most dangerous diseases in the world. Correct classification of skin lesions in the first step can help create clinical judgment by providing an optimal judgment of the disease. As a result, the odds of treating the spread of cancer early may be increased. However, the automatic classification of skin cancer is tough because of the imbalance in most skin cancer images used in training. Several methods based on deep learning have been broadly used recently in skin cancer classification to resolve the problems in classification and attain acceptable outcomes. Nevertheless, reviews containing the aforementioned borderline difficulties in skin melanoma classification are still rare. Thus, this paper presents a summary of the newest deep learning procedures for classifying skin cancer. This paper starts with a discussion of skin cancer types, followed by the presentation of a public dataset available for skin cancer. Subsequently, some pretrained models of CNN used for classification are highlighted. Finally, some opportunities for skin cancer, such as data imbalance and limitation, generative adversarial network, various data sets, and data augmentation, are summarized
Deep learning model for glioma, meningioma and pituitary classification
No full textOne of the common causes of death is a brain tumor. Because of the above mentioned, early detection of a brain tumor is critical for faster treatment, and therefore there are many techniques used to visualize a brain tumor. One of these techniques is magnetic resonance imaging (MRI). On the other hand, machine learning, deep learning, and convolutional neural network (CNN) are the state of art technologies in the recent years used in solving many medical image-related problems such as classification. In this research, three types of brain tumors were classified using magnetic resonance imaging namely glioma, meningioma, and pituitary gland on the based of CNN. The dataset used in this work includes 233 patients for a total of 3,064 contrast-enhanced T1 images. In this paper, a comparison is presented between the presented model and other models to demonstrate the superiority of our model over the others. Moreover, the difference in outcome between pre- and post-data preprocessing and augmentation was discussed. The highest accuracy metrics extracted from confusion matrices are; precision of 99.1% for pituitary, sensitivity of 98.7% for glioma, specificity of 99.1%, and accuracy of 99.1% for pituitary. The overall accuracy obtained is 96.1%
Erratum: Assessing the consistency of iPSC and animal models in cystic fibrosis modelling: A meta-analysis. (PLoS ONE (2022) 17:8 (e0272091) DOI: 10.1371/journal.pone.0272091)
No full textThere are errors in the Funding section. The correct Funding statement is: The author acknowledges Qatar National Library for funding the publication of the article
CCN3, POSTN, and PTHLH as potential key regulators of genomic integrity and cellular survival in iPSCs
No full textReprogramming human somatic cells into a pluripotent state, achieved through the activation of well-defined transcriptional factors known as OSKM factors, offers significant potential for regenerative medicine. While OSKM factors are a robust reprogramming method, efficiency remains a challenge, with only a fraction of cells undergoing successful reprogramming. To address this, we explored genes related to genomic integrity and cellular survival, focusing on iPSCs (A53T-PD1) that displayed enhanced colony stability. Our investigation had revealed three candidate genes CCN3, POSTN, and PTHLH that exhibited differential expression levels and potential roles in iPSC stability. Subsequent analyses identified various protein interactions for these candidate genes. POSTN, significantly upregulated in A53T-PD1 iPSC line, showed interactions with extracellular matrix components and potential involvement in Wnt signaling. CCN3, also highly upregulated, demonstrated interactions with TP53, CDKN1A, and factors related to apoptosis and proliferation. PTHLH, while upregulated, exhibited interactions with CDK2 and genes involved in cell cycle regulation. RT-qPCR validation confirmed elevated CCN3 and PTHLH expression in A53T-PD1 iPSCs, aligning with RNA-seq findings. These genes' roles in preserving pluripotency and cellular stability require further exploration. In conclusion, we identified CCN3, POSTN, and PTHLH as potential contributors to genomic integrity and pluripotency maintenance in iPSCs. Their roles in DNA repair, apoptosis evasion, and signaling pathways could offer valuable insights for enhancing reprogramming efficiency and sustaining pluripotency. Further investigations are essential to unravel the mechanisms underlying their actions.The author(s) declare financial support was received for the research, authorship, and/or publication of this article. Qatar University funded the publication of this article. This research work was supported by QRDI [grant number: UREP27-156-3-044].Scopu
Exploring the cost-effectiveness of high versus low perioperative fraction of inspired oxygen in the prevention of surgical site infections among abdominal surgery patients in three low- and middle-income countries
No full textBackground: This study assessed the potential cost-effectiveness of high (80–100%) vs low (21–35%) fraction of inspired oxygen (FiO2) at preventing surgical site infections (SSIs) after abdominal surgery in Nigeria, India, and South Africa. Methods: Decision-analytic models were constructed using best available evidence sourced from unbundled data of an ongoing pilot trial assessing the effectiveness of high FiO2, published literature, and a cost survey in Nigeria, India, and South Africa. Effectiveness was measured as percentage of SSIs at 30 days after surgery, a healthcare perspective was adopted, and costs were reported in US dollars (216 compared with 6 (95% confidence interval [CI]: −1) difference in costs. In India, the average cost for high FiO2 was 195 for low FiO2 leading to a −15 to −1164 compared with 93 (95% CI: −65) difference in costs. The high FiO2 arm had few SSIs, 7.33% compared with 8.38% for low FiO2, leading to a −1.05 (95% CI: −1.14 to −0.90) percentage point reduction in SSIs. Conclusion: High FiO2 could be cost-effective at preventing SSIs in the three countries but further data from large clinical trials are required to confirm this. © 2023 The Author
