Brain hemorrhage recognition using deep learning approach

Abstract

This study addresses the use of deep learning techniques, including Inception, ResNet, Xception, AlexNet, and MobileNet, for the identification of brain hemorrhages in medical imaging. Motivated by the crucial need for rapid and accurate diagnoses, the research proposes deeplearning as a transformational answer, overcoming limits in standard diagnostic procedures. Theinquiry dives into the nuances of brain hemorrhage identification, addressing technological, methodological, and practical issues inherent in the use of deep learning approaches. The studypredicts a large influence on society by increasing patient outcomes, boosting healthcareaccessible, and transforming healthcare dynamics. Ethical issues, such as patient privacy, permission, and algorithmic bias, are thoroughly investigated, connecting the studywithresponsible and transparent AI implementation. The environmental effect of deep learningsystems is analyzed, stressing measures for enhancing energy efficiency and implementingeco-friendly computing practices. The sustainability strategy provided in the research becomesaguiding light, assuring the ongoing relevance and good effect of the offered solutions. Inconclusion, the study indicates an important milestone in the development of medical imagingtechnology, delivering useful insights into the comparative performance of deep learning systems. Future work is proposed to further boost algorithmic performance, diversify datasets, improveinterpretability, allow real-world clinical integration, and address ethical and regulatoryframeworks. The complete character of this research, embracing socioeconomic, ethical, environmental, and sustainability factors, presents it as a holistic contribution to the areaofmedical diagnostics, supporting responsible and impactful technology deployment.