Attallah Omneya, Aslan Muhammet Fatih, Sabanci Kadir
Department of Electronics and Communications Engineering, College of Engineering and Technology, Arab Academy for Science, Technology and Maritime Transport, Alexandria 1029, Egypt.
Department of Electrical and Electronics Engineering, Karamanoglu Mehmetbey University, 70100 Karaman, Turkey.
Diagnostics (Basel). 2022 Nov 23;12(12):2926. doi: 10.3390/diagnostics12122926.
Among the leading causes of mortality and morbidity in people are lung and colon cancers. They may develop concurrently in organs and negatively impact human life. If cancer is not diagnosed in its early stages, there is a great likelihood that it will spread to the two organs. The histopathological detection of such malignancies is one of the most crucial components of effective treatment. Although the process is lengthy and complex, deep learning (DL) techniques have made it feasible to complete it more quickly and accurately, enabling researchers to study a lot more patients in a short time period and for a lot less cost. Earlier studies relied on DL models that require great computational ability and resources. Most of them depended on individual DL models to extract features of high dimension or to perform diagnoses. However, in this study, a framework based on multiple lightweight DL models is proposed for the early detection of lung and colon cancers. The framework utilizes several transformation methods that perform feature reduction and provide a better representation of the data. In this context, histopathology scans are fed into the ShuffleNet, MobileNet, and SqueezeNet models. The number of deep features acquired from these models is subsequently reduced using principal component analysis (PCA) and fast Walsh-Hadamard transform (FHWT) techniques. Following that, discrete wavelet transform (DWT) is used to fuse the FWHT's reduced features obtained from the three DL models. Additionally, the three DL models' PCA features are concatenated. Finally, the diminished features as a result of PCA and FHWT-DWT reduction and fusion processes are fed to four distinct machine learning algorithms, reaching the highest accuracy of 99.6%. The results obtained using the proposed framework based on lightweight DL models show that it can distinguish lung and colon cancer variants with a lower number of features and less computational complexity compared to existing methods. They also prove that utilizing transformation methods to reduce features can offer a superior interpretation of the data, thus improving the diagnosis procedure.
肺癌和结肠癌是导致人类死亡和发病的主要原因。它们可能在器官中同时发生，并对人类生命产生负面影响。如果癌症在早期未被诊断出来，很有可能会扩散到这两个器官。对这类恶性肿瘤进行组织病理学检测是有效治疗的关键组成部分之一。尽管这个过程漫长而复杂，但深度学习（DL）技术使其能够更快、更准确地完成，使研究人员能够在短时间内以更低的成本研究更多患者。早期的研究依赖于需要强大计算能力和资源的DL模型。其中大多数依赖单个DL模型来提取高维特征或进行诊断。然而，在本研究中，提出了一种基于多个轻量级DL模型的框架，用于肺癌和结肠癌的早期检测。该框架利用了几种变换方法来进行特征约简，并提供更好的数据表示。在这种情况下，将组织病理学扫描结果输入到ShuffleNet、MobileNet和SqueezeNet模型中。随后，使用主成分分析（PCA）和快速沃尔什 - 哈达玛变换（FHWT）技术减少从这些模型中获取的深度特征数量。接着，使用离散小波变换（DWT）融合从三个DL模型获得的经FWHT约简后的特征。此外，还将三个DL模型的PCA特征连接起来。最后，将经过PCA和FHWT - DWT约简与融合过程后减少的特征输入到四种不同的机器学习算法中，达到了99.6%的最高准确率。使用基于轻量级DL模型的所提出框架获得的结果表明，与现有方法相比，它能够以更少的特征数量和更低的计算复杂度区分肺癌和结肠癌变体。这些结果还证明，利用变换方法减少特征可以提供对数据的更好解释，从而改进诊断过程。
