Deep learning has been successfully applied in classification of white blood cells (WBCs), however, accuracy and processing time are found to be less than optimal hindering it from getting its full potential. This is due to imbalanced dataset, intra-class compactness, inter-class separability and overfitting problems. The main research idea is to enhance the classification and prediction accuracy of blood images while lowering processing time through the use of deep convolutional neural network (DCNN) architecture by using the modified loss function. The proposed system consists of a deep neural convolution network (DCNN) that will improve the classification accuracy by using modified loss function along with regularization. Firstly, images are pre-processed and fed through DCNN that contains different layers with different activation function for the feature extraction and classification. Along with modified loss function with regularization, weight function aids in the classification of WBCs by considering weights of samples belonging to each class for compensating the error arising due to imbalanced dataset. The processing time will be counted by each image to check the time enhancement. The classification accuracy and processing time are achieved using the dataset-master. Our proposed solution obtains better classification performance in the given dataset comparing with other previous methods. The proposed system enhanced the classification accuracy of 98.92% from 96.1% and a decrease in processing time from 0.354 to 0.216 s. Less time will be required by our proposed solution for achieving the model convergence with 9 epochs against the current convergence time of 13.5 epochs on average, epoch is the formation white blood cells (WBCs) and the development of granular cells. The proposed solution modified loss function to solve the adverse effect caused due to imbalance dataset by considering weight and use regularization technique for overfitting problem.

深度学习已成功应用于白细胞（WBC）的分类中，但是，发现准确性和处理时间不是最佳选择，这阻碍了其发挥其全部潜能。这是由于数据集不平衡，类内紧凑性，类间可分离性和过度拟合问题造成的。主要研究思想是通过使用改进的损失函数，通过使用深度卷积神经网络（DCNN）体系结构来提高血液图像的分类和预测精度，同时减少处理时间。拟议的系统由一个深度神经卷积网络（DCNN）组成，该网络将通过使用修正的损失函数和正则化来提高分类准确性。首先，图像经过预处理并通过DCNN馈送，该DCNN包含具有不同激活功能的不同图层，用于特征提取和分类。权重函数与经过修正的修正损失函数一起，通过考虑属于每个类别的样本权重来补偿WBC的分类，以补偿由于数据集不平衡而引起的误差。每个图像都会计算处理时间，以检查时间增强。使用数据集主数据可以实现分类的准确性和处理时间。与其他先前方法相比，我们提出的解决方案在给定的数据集中获得了更好的分类性能。提出的系统将分类准确率从96.1％提高到98.92％，并将处理时间从0.354 s减少到0.216 s。我们提出的解决方案将需要更少的时间来实现具有9个纪元的模型收敛，而当前的平均收敛时间为13.5个纪元，该纪元是白细胞的形成和粒状细胞的发育。提出的解决方案修改了损失函数，通过考虑权重并使用正则化技术解决过拟合问题，以解决由于数据集不平衡而引起的不利影响。