Deep Learning Techniques For Skull Stripping of Brain MR Images

One of the diseases on a global scale that causes the main reasons of death is lung cancer. It is considered one of the most lethal diseases in life. Early detection and diagnosis are essential for lung cancer and will provide effective therapy and achieve better outcomes for patients; in recent years, algorithms of Deep Learning have demonstrated crucial promise for their use in medical imaging analysis, especially in lung cancer identification. This paper includes a comparison between a number of different Deep Learning techniques-based models using Computed Tomograph image datasets with traditional Convolution Neural Networks and SequeezeNet models using X-ray data for the automated diagnosis of lung cancer. Although the simple details p

The convolutional neural networks (CNN) are among the most utilized neural networks in various applications, including deep learning. In recent years, the continuing extension of CNN into increasingly complicated domains has made its training process more difficult. Thus, researchers adopted optimized hybrid algorithms to address this problem. In this work, a novel chaotic black hole algorithm-based approach was created for the training of CNN to optimize its performance via avoidance of entrapment in the local minima. The logistic chaotic map was used to initialize the population instead of using the uniform distribution. The proposed training algorithm was developed based on a specific benchmark problem for optical character recog

Background: Complete analysis of facial profile should also include an evaluation of soft tissue morphology. Materials and Method:The sample consisted of 90 Iraqi adults (45 males and 45 females) aged 18-25 years from Baghdad city divided into 3 groups according to the ANB angle with 30 subjects in each group (15 males and 15 females) for class I, II and III. Lateral cephalometric radiograph was taken for each subject and 8 angular and 5 linear measurements were identified and determined, t-test, ANOVA and LSD test were used to compare between both genders and between different classes. Results:Showed that females had greater angular measurements and smaller linear measurements with more lip prominence than males in all classes, there was m

Background: Intense pulsed light (IPL) devices produce polychromatic incoherent high-intensity pulsed light with a specified wavelength spectrum, fluence, and pulse duration through the use of flashlamps and bandpass filters. Similar to lasers, IPL devices operate on the selective photothermolysis principle, with melanin acting as the chromophore. Despite this similarity, they are constructed differently and produce different amounts of light Aim of the study: To investigate the efficacy of IPL home-use device in hair reduction technique for women with unwanted facial hair. Subjects and methods: The study was conducted in Baghdad on forty-five female subjects with Fitzpatrick skin phototype (II to IV) and black, brown hair in a period of ei

This paper studies the adaptive coded modulation for coded OFDM system using punctured convolutional code, channel estimation, equalization and SNR estimation. The channel estimation based on block type pilot arrangement is performed by sending pilots at every sub carrier and using this estimation for a specific number of following symbols. Signal to noise ratio is estimated at receiver and then transmitted to the transmitter through feedback channel ,the transmitter according to the estimated SNR select appropriate modulation scheme and coding rate which maintain constant bit error rate
lower than the requested BER. Simulation results show that better performance is confirmed for target bit error rate (BER) of (10-3) as compared to c

The precise classification of DNA sequences is pivotal in genomics, holding significant implications for personalized medicine. The stakes are particularly high when classifying key genetic markers such as BRAC, related to breast cancer susceptibility; BRAF, associated with various malignancies; and KRAS, a recognized oncogene. Conventional machine learning techniques often necessitate intricate feature engineering and may not capture the full spectrum of sequence dependencies. To ameliorate these limitations, this study employs an adapted UNet architecture, originally designed for biomedical image segmentation, to classify DNA sequences.The attention mechanism was also tested LONG WITH u-Net architecture to precisely classify DNA sequences