8 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
Survey of the reptilian fauna of the Kingdom of Saudi Arabia: the Snake fauna of the central region
No full textCorresponding Author:
Mohammed K. Al-Sadoon
Department of Zoology, College of Science, King Saud University, P.O. Box 2455, Riyadh 11451, Saudi Arabia
Phone: +966-1-4675755
[email protected] snake fauna inhabiting the Central Region of the Kingdom of Saudi Arabia has been investigated by the collection and subsequent identification of211 specimens from various localities in the Reg-ion. Fourteen species belonging to five families: Boidae, Colubridae, Elapidae, Leptotyphlopidae and Viperidae were recorded. The following species were reported from the Central Region for the first time: Atractaspis microlepidota engaddensis, Leptotyphlops macrorhynchus, Spalerosophis diadem a cliffordi, Telescopus dhara dhara, Coluber ventromaculatus and Eryx jayakari. The geographical distribution of the snakes is presented together with notes on their habitats, and general biology
Thermal ecology and activity of the sand fish lizard, scincus mitranus (Scincidae) in Central Arabia
No full textAuthors: Al-Johany Awadh M.& Al-Sadoon, Mohamed K. From Department of Zoology,
Author: Al-Farraj Saud A., From Department of Biology
Riyadh, Teachers' College, King Saud University, P.O. Box 4341,
Riyadh 11491. Saudi ArabiaThermal ecology and activity of Scincus mitranus was investigated during winter and summer seasons. Emergence and basking behavior and seasonal activity were studied and analyzed. The lizard was active throughout the year except during cold spells of winter. However, it was found that during winter, daily activity was unimodal, which contrasted with the bimodal pattern during summer. Selected body temperature, critical minimum and maximum were studied and determined in the laboratory
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%
The effects of walterinnesia aegyptia venom on the serum and tissue metabolites and on some enzyme activities in albino rats. III-effect on lipid metabolism and two dehydrogenases
No full textAn LD 50 (0.2 mglkg) dose of Walterinnesia aegyptia venom to albino rats causes a significant decrease in total serum lipids, triacylglycerols and total cholseterol, with a decline in both glucose-6-phosphate dehydrogenase (G6PDH) and lactate dehydrogenase (LDH) activities, possibly due to lipolytic and specific inhibitors present in the venom. Accumulation of totallipids was observed in the liver, kidneys, heart and brain of envenomated animals with a subsequent insignificant decrease in total cholesterol and triacylglycerols, except in the liver, where the decrease was significant. This suggests mobilization of lipids from peripheral tissues to these organs accompanied by release of other components of totallipids such as phospholipids and fatty acids. Deficiency in the pentose phosphate pathway (PPP) , disturbance in the oxidoreduction system and altered energy metabolism were also detected. Variations in enzyme activities were also seen with the G6PDH level which generally elevated in the all organs studied, except in brain. The level of LDH declined in liver and kidney, but increased in heart and brain. This may suggest prevalence of anaerobic conditions in such organs as a result of venom detoxication.Corresponding Author:
Dr.Ibrahim A. A. Al-Jammaz, Professor, Department of Biology, College Of Teaching, King Saud University, PO Box 4341, Riyadh 11491, Saudi Arabia. Email: [email protected]
Pull-out behaviour of straight and hooked-end steel fibres under elevated temperatures
No full textThis paper presents the results of an experimental investigation into the effect of elevated temperature on the steel fibre-matrix bond characteristics. A series of pull-out tests on straight and hooked-end fibres embedded in four different cementitious matrixes, namely normal strength concrete (NSC), medium strength concrete (MSC), high strength concrete (HSC) and ultra-high performance mortar (UHPM) were performed. Ninety days after casting, the specimens were heated to target temperatures of 100, 200, 300, 400, 500, 600, 700 and 800 °C, respectively. The initial and residual thermal and mechanical properties of the concrete were investigated. It was shown that while the variation in compressive strength and pull-out response for different temperatures is relatively small up to 400 °C, further increase in temperature results in a reduction in the pull-out strength, especially for the temperature > 600 °C. At 800 °C, the maximum pull-out load of the hooked-end fibres with NSC, MSC and HSC decreased by 54%, 64% and 56%, respectively.The first author gratefully acknowledges the financial support of the Ministry of Higher Education and Scientific Research of Iraqi Government for this Ph.D. project
Bond-slip behaviour of steel fibres in concrete after exposure to elevated temperatures
No full textThe bond-slip mechanisms, associated with the pull-out behaviour of steel fibres embedded in concrete after exposure to elevated temperatures, are experimentally investigated. A series of pull-out tests have been performed on straight and hooked-end steel fibres embedded in four different types of concrete, namely, normal strength concrete (NSC), medium strength concrete (MSC), high strength concrete (HSC) and ultra-high performance mortar (UHPM). Ninety days after casting, the specimens were heated to a target temperature of either 100, 200, 300, 400, 500, 600, 700 or 800 °C. The effect of temperature on the mechanical and thermal properties of the steel fibres and concrete was also studied. The results showed that the bond behaviour of straight fibres is significantly influenced by heating. The influence of elevated temperatures on the bond characteristic of hooked-end fibre was twofold: the bond strength does not vary significantly for all matrixes in 20–400 °C, while the bond dramatically degraded in 400–800 °C, especially at temperatures greater than 600 °C. The reduction in bond strength at elevated temperatures is found to be strongly related to the degradation in properties of the constituent materials, i.e. the fibres and concrete.The first author gratefully acknowledges the financial support of the Ministry of Higher Education and Scientific Research of Iraqi Government for this Ph.D. project
