The use of a binary classifier like the sigmoid function and loss functions reduces the accuracy of deep learning algorithms. This research aims to increase the accuracy of detecting and classifying oral tumours within a reduced processing time. The proposed system consists of a Convolutional neural network with a modified loss function to minimise the error in predicting and classifying oral tumours by reducing the overfitting of the data and supporting multi-class classification. The proposed solution was tested on data samples from multiple datasets with four kinds of oral tumours. The averages of the different accuracy values and processing times were calculated to derive the overall accuracy. Based on the obtained results, the proposed solution achieved an overall accuracy of 96.5%, which was almost 2.0% higher than the state-of-the-art solution with 94.5% accuracy. Similarly, the processing time has been reduced by 30–40 milliseconds against the state-of-the-art solution. The proposed system is focused on detecting oral tumours in the given magnetic resonance imaging (MRI) scan and classifying whether the tumours are benign or malignant. This study solves the issue of over fitting data during the training of neural networks and provides a method for multi-class classification.

像S形函数和损失函数这样的二进制分类器的使用会降低深度学习算法的准确性。这项研究旨在提高在减少的处理时间内对口腔肿瘤进行检测和分类的准确性。所提出的系统由卷积神经网络组成，该卷积神经网络具有修改后的损失函数，可通过减少数据的过拟合并支持多类别分类来最大程度地减少预测和分类口腔肿瘤的误差。所提出的解决方案在来自具有四种口腔肿瘤的多个数据集的数据样本上进行了测试。计算不同精度值和处理时间的平均值，以得出整体精度。根据获得的结果，提出的解决方案实现了96.5％的整体精度，几乎是2。比最先进的解决方案高出0％，准确度达到94.5％。同样，与最新解决方案相比，处理时间减少了30-40毫秒。拟议的系统专注于在给定的磁共振成像（MRI）扫描中检测口腔肿瘤，并对肿瘤是良性还是恶性进行分类。这项研究解决了神经网络训练过程中数据过度拟合的问题，并提供了一种用于多类分类的方法。