Histopathological Analysis for Detecting Lung and Colon Cancer Malignancies Using Hybrid Systems with Fused Features

• Published in: Bioengineering (Basel) Vol. 10; no. 3; p. 383
• Main Authors: Al-Jabbar, Mohammed, Alshahrani, Mohammed, Senan, Ebrahim Mohammed, Ahmed, Ibrahim Abdulrab
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.03.2023; MDPI
• Subjects: Accuracy; Algorithms; ANN; Artificial intelligence; Bioengineering; Biopsy; Cancer; Colon cancer; Colorectal cancer; Datasets; Deep learning; Developing countries; Diagnosis; FCH; GLCM; GoogLeNet; Health care facilities; Hybrid systems; Image contrast; LDCs; lung and colon cancer; Lung cancer; Machine learning; Malignancy; Medical diagnosis; Medical imaging; Medical personnel; Methods; Tumors; VGG-19; United States; DWT; ANN; lung and colon cancer; VGG-19; GoogLeNet; FCH; LBP; GLCM; PCA
• ISSN: 2306-5354; 2306-5354
• DOI: 10.3390/bioengineering10030383

Lung and colon cancer are among humanity’s most common and deadly cancers. In 2020, there were 4.19 million people diagnosed with lung and colon cancer, and more than 2.7 million died worldwide. Some people develop lung and colon cancer simultaneously due to smoking which causes lung cancer, leading to an abnormal diet, which also causes colon cancer. There are many techniques for diagnosing lung and colon cancer, most notably the biopsy technique and its analysis in laboratories. Due to the scarcity of health centers and medical staff, especially in developing countries. Moreover, manual diagnosis takes a long time and is subject to differing opinions of doctors. Thus, artificial intelligence techniques solve these challenges. In this study, three strategies were developed, each with two systems for early diagnosis of histological images of the LC25000 dataset. Histological images have been improved, and the contrast of affected areas has been increased. The GoogLeNet and VGG-19 models of all systems produced high dimensional features, so redundant and unnecessary features were removed to reduce high dimensionality and retain essential features by the PCA method. The first strategy for diagnosing the histological images of the LC25000 dataset by ANN uses crucial features of GoogLeNet and VGG-19 models separately. The second strategy uses ANN with the combined features of GoogLeNet and VGG-19. One system reduced dimensions and combined, while the other combined high features and then reduced high dimensions. The third strategy uses ANN with fusion features of CNN models (GoogLeNet and VGG-19) and handcrafted features. With the fusion features of VGG-19 and handcrafted features, the ANN reached a sensitivity of 99.85%, a precision of 100%, an accuracy of 99.64%, a specificity of 100%, and an AUC of 99.86%.