论文标题
皮肤科医生与神经网络
Dermatologist vs Neural Network
论文作者
论文摘要
总的来说，癌症是非常致命的。及时治疗任何癌症是挽救生命的关键。皮肤癌也不例外。全世界每年都有成千上万的皮肤癌病例。一年中发现了123,000例致命黑色素瘤病例。事实证明，由于臭氧层的降解，这一巨大数量被证明是阳光中存在大量紫外线的原因。如果在早期未发现，皮肤癌可能会导致患者死亡。无法获得适当的资源，例如专家皮肤科医生，最先进的测试设施以及快速的活检结果，使研究人员开发了一种可以解决上述问题的技术。深度学习就是一种提供非凡结果的方法。这项研究中提出的卷积神经网络执行了所有验证的模型。我们在HAM10000数据集上训练了模型，该数据集提供了属于7类皮肤病的10015张图像。我们提出的模型的精度为89％。该模型可以准确地预测致命的黑色素瘤皮肤癌。希望这项研究可以通过使用我们建议的研究来弥合差距，可以帮助挽救人们的生命，而这些研究无法获得适当的皮肤病学资源。
Cancer, in general, is very deadly. Timely treatment of any cancer is the key to saving a life. Skin cancer is no exception. There have been thousands of Skin Cancer cases registered per year all over the world. There have been 123,000 deadly melanoma cases detected in a single year. This huge number is proven to be a cause of a high amount of UV rays present in the sunlight due to the degradation of the Ozone layer. If not detected at an early stage, skin cancer can lead to the death of the patient. Unavailability of proper resources such as expert dermatologists, state of the art testing facilities, and quick biopsy results have led researchers to develop a technology that can solve the above problem. Deep Learning is one such method that has offered extraordinary results. The Convolutional Neural Network proposed in this study out performs every pretrained models. We trained our model on the HAM10000 dataset which offers 10015 images belonging to 7 classes of skin disease. The model we proposed gave an accuracy of 89%. This model can predict deadly melanoma skin cancer with a great accuracy. Hopefully, this study can help save people's life where there is the unavailability of proper dermatological resources by bridging the gap using our proposed study.
