Enhancing Breast Cancer Detection: Leveraging Convolutional Neural Networks

Mohith K P
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Abstract

Abstract: Breast cancer continues to be a critical health concern, necessitating early detection and accurate classification for effective treatment. This study presents a comparative analysis between a custom-designed Convolutional Neural Network (CNN) and the pre-trained DenseNet121 model for breast cancer detection and classification. We compiled a comprehensive dataset of breast cancer images and applied appropriate preprocessing techniques to optimize the input for the models. The dataset was divided into training, validation, and testing sets to evaluate the models' performance. The CNN model comprises multiple convolutional and pooling layers followed by fully connected layers for classification, while the DenseNet121 model is fine-tuned specifically for breast cancer detection. The models were evaluated based on metrics such as accuracy, precision, recall, F1 score, and AUC-ROC. The DenseNet121 model outperformed the custom CNN model, achieving higher accuracy and reliability. For wider accessibility, we integrated the superior DenseNet121 model into a user-friendly web-based interface using Python Flask, enabling real-time breast cancer predictions. Ethical considerations were paramount, ensuring data privacy, security, and transparency in all model predictions. This study highlights the effectiveness of the DenseNet121 model and contributes to improved breast cancer diagnosis and patient care.
加强乳腺癌检测:利用卷积神经网络
摘要:乳腺癌仍然是一个严重的健康问题,需要早期检测和准确分类以进行有效治疗。本研究对定制设计的卷积神经网络(CNN)和预训练的 DenseNet121 模型在乳腺癌检测和分类方面进行了比较分析。我们编制了一个全面的乳腺癌图像数据集,并应用适当的预处理技术来优化模型的输入。数据集被分为训练集、验证集和测试集,以评估模型的性能。CNN 模型包括多个卷积层和池化层,然后是用于分类的全连接层,而 DenseNet121 模型则专门针对乳腺癌检测进行了微调。我们根据准确率、精确度、召回率、F1 分数和 AUC-ROC 等指标对这些模型进行了评估。DenseNet 121 模型的表现优于定制的 CNN 模型,获得了更高的准确度和可靠性。为了扩大应用范围,我们使用 Python Flask 将卓越的 DenseNet 121 模型集成到用户友好的网络界面中,实现了乳腺癌的实时预测。道德方面的考虑是最重要的,以确保所有模型预测的数据隐私性、安全性和透明度。这项研究凸显了 DenseNet121 模型的有效性,有助于改善乳腺癌诊断和患者护理。
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