Albahli Saleh, Nazir Tahira
Department of Information Technology, College of Computer, Qassim University, Buraydah 51452, Saudi Arabia.
Faculty of Computing, Riphah International University, Islamabad 44600, Pakistan.
Bioengineering (Basel). 2023 Sep 29;10(10):1147. doi: 10.3390/bioengineering10101147.
Signet ring cell (SRC) carcinoma is a particularly serious type of cancer that is a leading cause of death all over the world. SRC carcinoma has a more deceptive onset than other carcinomas and is mostly encountered in its later stages. Thus, the recognition of SRCs at their initial stages is a challenge because of different variants and sizes and illumination changes. The recognition process of SRCs at their early stages is costly because of the requirement for medical experts. A timely diagnosis is important because the level of the disease determines the severity, cure, and survival rate of victims. To tackle the current challenges, a deep learning (DL)-based methodology is proposed in this paper, i.e., custom CircleNet with ResNet-34 for SRC recognition and classification. We chose this method because of the circular shapes of SRCs and achieved better performance due to the CircleNet method. We utilized a challenging dataset for experimentation and performed augmentation to increase the dataset samples. The experiments were conducted using 35,000 images and attained 96.40% accuracy. We performed a comparative analysis and confirmed that our method outperforms the other methods.
印戒细胞（SRC）癌是一种特别严重的癌症类型，是全球主要的死亡原因之一。SRC癌的发病比其他癌症更具欺骗性，大多在晚期才被发现。因此，由于其变体、大小和光照变化不同，在初始阶段识别SRC具有挑战性。由于需要医学专家，SRC早期识别过程成本高昂。及时诊断很重要，因为疾病的程度决定了患者的严重程度、治愈率和生存率。为应对当前挑战，本文提出一种基于深度学习（DL）的方法，即用于SRC识别和分类的定制CircleNet与ResNet-34相结合的方法。我们选择这种方法是因为SRC的圆形形状，并且由于CircleNet方法取得了更好的性能。我们使用具有挑战性的数据集进行实验，并进行扩充以增加数据集样本。实验使用了35000张图像，准确率达到了96.40%。我们进行了对比分析，并证实我们的方法优于其他方法。
