Alwakid Ghadah, Gouda Walaa, Humayun Mamoona, Jhanjhi N Z
Department of Computer Science, College of Computer and Information Sciences, Jouf University, Sakakah 72341, Saudi Arabia.
Department of Electrical Engineering, Shoubra Faculty of Engineering, Benha University, Cairo 11672, Egypt.
Diagnostics (Basel). 2023 May 22;13(10):1815. doi: 10.3390/diagnostics13101815.
When it comes to skin tumors and cancers, melanoma ranks among the most prevalent and deadly. With the advancement of deep learning and computer vision, it is now possible to quickly and accurately determine whether or not a patient has malignancy. This is significant since a prompt identification greatly decreases the likelihood of a fatal outcome. Artificial intelligence has the potential to improve healthcare in many ways, including melanoma diagnosis. In a nutshell, this research employed an Inception-V3 and InceptionResnet-V2 strategy for melanoma recognition. The feature extraction layers that were previously frozen were fine-tuned after the newly added top layers were trained. This study used data from the HAM10000 dataset, which included an unrepresentative sample of seven different forms of skin cancer. To fix the discrepancy, we utilized data augmentation. The proposed models outperformed the results of the previous investigation with an effectiveness of 0.89 for Inception-V3 and 0.91 for InceptionResnet-V2.
说到皮肤肿瘤和癌症，黑色素瘤是最常见且致命的疾病之一。随着深度学习和计算机视觉技术的进步，现在能够快速准确地判断患者是否患有恶性肿瘤。这一点非常重要，因为及时识别能大大降低致命后果的可能性。人工智能有潜力在许多方面改善医疗保健，包括黑色素瘤诊断。简而言之，本研究采用了Inception-V3和InceptionResnet-V2策略进行黑色素瘤识别。在新添加的顶层经过训练后，对之前冻结的特征提取层进行了微调。本研究使用了HAM10000数据集的数据，该数据集包含了七种不同形式皮肤癌的非代表性样本。为了解决差异问题，我们采用了数据增强技术。所提出的模型表现优于之前的研究结果，Inception-V3的有效性为0.89，InceptionResnet-V2的有效性为0.91。
