DEPLOYMENT OF ENHANCED DEEP LEARNING MODEL WITH THE BEST ESTIMATORS ON OPTIMIZERS AND ACTIVATION FUNCTIONS FOR HEALTHCARE IN WEB APPLICATION

Elluru Sai Harshitha, K. Vijayalakshmi
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Abstract

Breast cancer and brain tumor stand as leading global causes of mortality. Brain tumor uses Magnetic Resonance Imaging (MRI) which offers superior clarity in visualizing brain structures compared to other imaging modalities, while Breast cancer uses ultrasonography (US) serves as a common tool for detecting breast cancer despite its inherent limitations in image quality. Motion artifacts frequently hinder MRI scans, necessitating skilled radiologists for accurate interpretation. Computer-aided diagnosis (CAD) systems driven by artificial intelligence, present a promising solution by consistently assisting radiologists in analyzing US images. Convolutional neural networks (CNNs) leverage various optimizers like Adam and Stochastic Gradient Descent (SGD), RMSprop, Adagrad, and Adadelta as well as activation functions including PReLu, LeakyReLu, Elu, and ReLu for their construction and training. The comparative analysis highlights the importance of optimizers and activation functions in deep learning algorithms for predicting brain tumors and breast cancer. The Adam optimizer combined with the ReLU activation function achieved an accuracy of 85% for breast cancer prediction, while RMSprop combined with ReLU activation function achieved a higher accuracy of 93% for brain tumor classification. From this research, considerable deep learning configurations are identified for both breast cancer and brain tumor prediction, facilitating more precise and efficient diagnoses. The comparative analysis provides valuable insights for those involved in medical imaging applications. Furthermore, The CNN model is deployed in web interface using flask framework to streamline the integration of these models into healthcare systems. This interface simplifies the input of medical data including image data and provides real-time predictions. KEYWORDS—Convolutional neural networks, Comparative Analysis, Activation functions, Optimizers, Ultrasound images, MRI images, Breast cancer, Brain tumor, medical imaging applications, FLASK.
利用优化器和激活函数的最佳估算器部署增强型深度学习模型,用于网络应用中的医疗保健领域
乳腺癌和脑肿瘤是导致全球死亡的主要原因。脑肿瘤使用磁共振成像(MRI),与其他成像模式相比,磁共振成像能更清晰地显示脑部结构,而乳腺癌使用超声波成像(US),尽管超声波成像在图像质量方面存在固有的局限性,但它仍是检测乳腺癌的常用工具。运动伪影经常阻碍核磁共振成像扫描,需要技术娴熟的放射科医生进行准确解读。由人工智能驱动的计算机辅助诊断(CAD)系统通过持续协助放射科医生分析 US 图像,提供了一种前景广阔的解决方案。卷积神经网络(CNN)利用各种优化器,如 Adam 和随机梯度下降(SGD)、RMSprop、Adagrad 和 Adadelta,以及激活函数,包括 PReLu、LeakyReLu、Elu 和 ReLu,进行构建和训练。对比分析凸显了优化器和激活函数在预测脑肿瘤和乳腺癌的深度学习算法中的重要性。Adam 优化器与 ReLU 激活函数相结合的乳腺癌预测准确率达到了 85%,而 RMSprop 与 ReLU 激活函数相结合的脑肿瘤分类准确率更高,达到了 93%。这项研究为乳腺癌和脑肿瘤预测确定了相当可观的深度学习配置,有助于更精确、更高效的诊断。对比分析为医疗成像应用领域的相关人员提供了宝贵的见解。此外,CNN 模型使用 flask 框架部署在网络接口中,以简化这些模型与医疗系统的集成。关键词:卷积神经网络 比较分析 激活函数 优化器 超声图像 核磁共振图像 乳腺癌 脑肿瘤 医学影像应用 FLASK。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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