Today’s modern medical imaging research faces the challenge of detecting brain tumor through Magnetic Resonance Images (MRI). Normally, to produce images of soft tissue of human body, MRI images are used by experts. It is used for analysis of human organs to replace surgery. For brain tumor detection, image segmentation is required. For this purpose, the brain is partitioned into two distinct regions. This is considered to be one of the most important but difficult part of the process of detecting brain tumor. Hence, it is highly necessary that segmentation of the MRI images must be done accurately before asking the computer to do the exact diagnosis. Earlier, a variety of algorithms were developed for segmentation of MRI images by using different tools and techniques. However, this paper presents a comprehensive review of the methods and techniques used to detect brain tumor through MRI image segmentation. Lastly, the paper concludes with a concise discussion and provides a direction toward the upcoming trend of more advanced research studies on brain image segmentation and Tumor detection.
Ibn al-Anbari, however, turned to Abu Hatim al-Sijistani in eleven places of his book ((clarify the endowment and start)). The study showed that the right and right was with the son of Anbari and that what went to Sistani saying uniqueness, and contrary to the vast majority of scientists. The study also showed that the science of grammar affects the endowment and initiation, as the study showed that the science of endowment and initiation is one of the important sciences of the Koran, which must be carefully received, researchers.
In the coming decade, a substantial rise in energy consumption within the buildings sector is predicted to lead to a 30% increase in greenhouse gas emissions. The choice of materials for building envelopes significantly influences the overall energy demand of HVAC systems, which contribute significantly to electricity usage. To enhance compatibility between grey clay and straw, a suggested approach involves using a composite material comprising rice water and grey clay, enriched with a high proportion of rice straw and soaked in rice water. This environmentally friendly technique yields a green construction material capable of reducing energy consumption in HVAC systems by up to 35.6% over a 24-h period. The potential energy savings of this
... Show MoreThis article briefly analyzing contemporary works appeared in theater writer from Latin America, which comes within the theme of "power." Latin American Literature, such as two-way extremely clear: the vanguard of social and attention, have arrived at certain moments to some extent be regarded as a two-way rival. That desire to participate in the revolution of expression and artistic significance, has appeared evident in the literature of Latin America in the late nineteenth century and ended in the third decade of the twentieth century. The writers that stage would prefer not to serve the objectives of the revolution of Arts own but the objectives of social and political revolution that stimulate the world. These acts were issued
... Show MoreTumor necrosis factor-alpha (TNF-α) inhibitors are widely used as first-line treatments for moderate to severe plaque psoriasis, yet clinical responses vary considerably among patients despite their overall safety and efficacy. Genetic factors that influence the effectiveness of biologic therapies may contribute to this variability. This review, conducted between May and ...
Community detection is one of the most fundamental applications in understanding the structure of complicated networks. Furthermore, it is an important approach to identifying closely linked clusters of nodes that may represent underlying patterns and relationships. Networking structures are highly sensitive in social networks, requiring advanced techniques to accurately identify the structure of these communities.Most conventional algorithms for detecting communities perform inadequately with complicated networks. In addition, they miss out on accurately identifying clusters. Since single-objective optimization cannot always generate accurate and comprehensive results, as multi-objective optimization can.Therefore,we utilized two objective
... Show MoreEarly and accurate detection of COVID-19 from chest computed tomography (CT) scans are becoming essential for effective clinical decision-making and disease control. This study is proposing a robust deep learning framework that integrates a convolutional self-attention network (CSAN), gamma correction for image enhancement, and a voting-based ensemble classifier to improving diagnostic performance. The model is being evaluated on a dataset of 2,271 CT images and is achieving an accuracy of 95.12%, sensitivity of 97.25%, specificity of 98.11%, F1-score of 96.46%, and area under the curve (AUC) of 0.977. Experimental results are demonstrating that the proposed method significantly surpasses baseline models, including standalone CSAN,
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