Department of Computer Science and Engineering, GITAM University, Bangalore, India.
Department of Computer Science and Engineering, AMET University, Chennai, India.
Microsc Res Tech. 2024 Aug;87(8):1789-1809. doi: 10.1002/jemt.24559. Epub 2024 Mar 22.
Skin cancer is a terrifying disorder that affects all individuals. Due to the significant increase in the rate of melanoma skin cancer, early detection of skin cancer is now more critical than ever before. Malignant melanoma is one of the most serious forms of skin cancer, and it is caused by abnormal melanocyte cell growth. In recent years, skin cancer predictive categorization has become more accurate and predictive due to multiple deep learning algorithms. Malignant melanoma is diagnosed using the Recurrent Convolution Neural Network-Long Short-Term Memory (RCNN-LSTM), which is one of the deep learning classification approaches. Using the International Skin Image Collection and the RCNN-LSTM, the data are categorized and analyzed to gain a better understanding of skin cancer. The method begins with data preprocessing, which prepares the dataset for classification. Additionally, the RCNN is employed to extract the features that are vital to the prediction process. The LSTM is accountable for the final step, classification. There are further factors to examine, such as the precision of 94.60%, the sensitivity of 95.67%, and the F1-score of 95.13%. Other benefits of the suggested study include shorter prediction durations of 95.314, 122.530, and 131.205 s and lower model loss of 0.25%, 0.19%, and 0.15% for input sizes 10, 15, and 20, respectively. Three datasets had a reduced categorization error of 5.11% and an accuracy of 95.42%. In comparison to previous approaches, the work discussed here produces superior outcomes. RESEARCH HIGHLIGHTS: Recurrent convolutional neural network (RCNN) deep learning approach for optimizing time prediction and error classification in early melanoma detection. It extracts a high number of specific features from the skin disease image, making the classification process easier and more accurate. To reduce classification errors in accurately detecting melanoma, context dependency is considered in this work. By accounting for context dependency, the deprivation state is avoided, preventing performance degradation in the model. To minimize melanoma detection model loss, a skin disease image augmentation or regularization process is performed in this work. This strategy improves the accuracy of the model when applied to fresh, previously unobserved data.
皮肤癌是一种可怕的疾病，影响所有人群。由于黑色素瘤皮肤癌发病率的显著增加，早期发现皮肤癌比以往任何时候都更加重要。恶性黑色素瘤是最严重的皮肤癌之一，由异常黑素细胞生长引起。近年来，由于多种深度学习算法，皮肤癌预测分类变得更加准确和具有预测性。恶性黑色素瘤的诊断使用的是递归卷积神经网络-长短期记忆（RCNN-LSTM），这是一种深度学习分类方法之一。该方法使用国际皮肤图像库和 RCNN-LSTM 对数据进行分类和分析，以更好地了解皮肤癌。该方法首先进行数据预处理，为分类准备数据集。此外，还使用 RCNN 提取对预测过程至关重要的特征。LSTM 负责最后一步，即分类。还有其他因素需要检查，例如 94.60%的精度、95.67%的敏感性和 95.13%的 F1 分数。该研究的其他好处包括预测时间分别缩短至 95.314、122.530 和 131.205 秒，输入大小分别为 10、15 和 20 时模型损失分别降低至 0.25%、0.19%和 0.15%。三个数据集的分类错误减少了 5.11%，准确率达到了 95.42%。与之前的方法相比，这里讨论的工作产生了更好的结果。研究亮点：用于优化早期黑色素瘤检测中时间预测和错误分类的递归卷积神经网络（RCNN）深度学习方法。它从皮肤疾病图像中提取大量特定特征，使分类过程更加容易和准确。为了减少在准确检测黑色素瘤时的分类错误，这项工作考虑了上下文相关性。通过考虑上下文相关性，可以避免剥夺状态，防止模型性能下降。为了最小化黑色素瘤检测模型的损失，这项工作对皮肤疾病图像进行了扩充或正则化处理。当应用于新的、以前未观察到的数据时，这种策略可以提高模型的准确性。
