A Machine Learning Approach to Diagnosing Lung and Colon Cancer Using a Deep Learning-Based Classification Framework

The field of Medicine and Healthcare has attained revolutionary advancements in the last forty years. Within this period, the actual reasons behind numerous diseases were unveiled, novel diagnostic methods were designed, and new medicines were developed. Even after all these achievements, diseases like cancer continue to haunt us since we are still vulnerable to them. Cancer is the second leading cause of death globally; about one in every six people die suffering from it. Among many types of cancers, the lung and colon variants are the most common and deadliest ones. Together, they account for more than 25% of all cancer cases. However, identifying the disease at an early stage significantly improves the chances of survival. Cancer diagnosis can be automated by using the potential of Artificial Intelligence (AI), which allows us to assess more cases in less time and cost. With the help of modern Deep Learning (DL) and Digital Image Processing (DIP) techniques, this paper inscribes a classification framework to differentiate among five types of lung and colon tissues (two benign and three malignant) by analyzing their histopathological images. The acquired results show that the proposed framework can identify cancer tissues with a maximum of 96.33% accuracy. Implementation of this model will help medical professionals to develop an automatic and reliable system capable of identifying various types of lung and colon cancers.

[1]  Paul J. Perry,et al.  A Review for the Clinician , 2002 .

[2]  Giovanni Ramponi,et al.  A cubic unsharp masking technique for contrast enhancement , 1998, Signal Process..

[3]  Bijaya Kumar Hatuwal,et al.  Lung Cancer Detection Using Convolutional Neural Network on Histopathological Images , 2020 .

[4]  Nasir M. Rajpoot,et al.  Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images , 2016, IEEE Trans. Medical Imaging.

[5]  Kuntal Kumar Pal,et al.  Preprocessing for image classification by convolutional neural networks , 2016, 2016 IEEE International Conference on Recent Trends in Electronics, Information & Communication Technology (RTEICT).

[6]  Luis Bote-Curiel,et al.  Deep learning to find colorectal polyps in colonoscopy: A systematic literature review , 2020, Artif. Intell. Medicine.

[7]  Bin Sheng,et al.  Computer-Assisted Decision Support System in Pulmonary Cancer detection and stage classification on CT images , 2018, J. Biomed. Informatics.

[8]  Roy D. Wallen,et al.  The Illustrated Wavelet Transform Handbook , 2004 .

[9]  Nima Tajbakhsh,et al.  Automatic polyp detection in colonoscopy videos , 2017, Medical Imaging.

[10]  Quan Wang,et al.  An Efficient Approach for Polyps Detection in Endoscopic Videos Based on Faster R-CNN , 2018, 2018 24th International Conference on Pattern Recognition (ICPR).

[11]  Mehedi Masud,et al.  A Novel Method to Identify Pneumonia through Analyzing Chest Radiographs Employing a Multichannel Convolutional Neural Network , 2020, Sensors.

[12]  Kenji Suzuki.  A review of computer-aided diagnosis in thoracic and colonic imaging. , 2012, Quantitative imaging in medicine and surgery.

[13]  D. Rubin,et al.  Radiomics and Radiogenomics , 2019, Machine and Deep Learning in Oncology, Medical Physics and Radiology.

[14]  Amr Tolba,et al.  Automatic detection of lung cancer from biomedical data set using discrete AdaBoost optimized ensemble learning generalized neural networks , 2019, Neural Computing and Applications.

[15]  Suat Toraman,et al.  Classification of the likelihood of colon cancer with machine learning techniquesusing FTIR signals obtained from plasma , 2019, Turkish J. Electr. Eng. Comput. Sci..

[16]  Yinghuan Shi,et al.  Multimodal Sparse Representation-Based Classification for Lung Needle Biopsy Images , 2013, IEEE Transactions on Biomedical Engineering.

[17]  D. Chicco,et al.  The advantages of the Matthews correlation coefficient (MCC) over F1 score and accuracy in binary classification evaluation , 2020, BMC Genomics.

[18]  K. Gunavathi,et al.  Lung cancer classification using neural networks for CT images , 2014, Comput. Methods Programs Biomed..

[19]  Nader Karimi,et al.  Classification of Informative Frames in Colonoscopy Videos Using Convolutional Neural Networks with Binarized Weights , 2018, 2018 40th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[20]  S. U. K. Bukhari,et al.  The Histological Diagnosis of Colonic Adenocarcinoma by Applying Partial Self Supervised Learning , 2020, medRxiv.

[21]  Anselmo Cardoso de Paiva,et al.  Classification of patterns of benignity and malignancy based on CT using topology-based phylogenetic diversity index and convolutional neural network , 2018, Pattern Recognit..

[22]  Victor Hugo C. de Albuquerque,et al.  Lung nodule malignancy classification in chest computed tomography images using transfer learning and convolutional neural networks , 2018, Neural Computing and Applications.

[23]  Wenqing Sun,et al.  Automatic feature learning using multichannel ROI based on deep structured algorithms for computerized lung cancer diagnosis , 2017, Comput. Biol. Medicine.

[24]  Prateek Prasanna,et al.  Radiomics and radiogenomics in lung cancer: A review for the clinician. , 2018, Lung cancer.

[25]  Subaji Mohan,et al.  ROI-based feature learning for efficient true positive prediction using convolutional neural network for lung cancer diagnosis , 2020, Neural Computing and Applications.

[26]  Melissa L McPheeters,et al.  Accuracy of FDG-PET to diagnose lung cancer in areas with infectious lung disease: a meta-analysis. , 2014, JAMA.

[27]  A. Mosig,et al.  Label-free classification of colon cancer grading using infrared spectral histopathology. , 2016, Faraday discussions.

[28]  Deepa Gupta,et al.  Colon Cancer Prediction On Different Magnified Colon Biopsy Images , 2018, 2018 Tenth International Conference on Advanced Computing (ICoAC).

[29]  Nikhil Ketkar,et al.  Convolutional Neural Networks , 2021, Deep Learning with Python.

[30]  Wei Shen,et al.  Multi-crop Convolutional Neural Networks for lung nodule malignancy suspiciousness classification , 2017, Pattern Recognit..

[31]  D. Hubel,et al.  Receptive fields and functional architecture of monkey striate cortex , 1968, The Journal of physiology.

[32]  Jürgen Schmidhuber,et al.  Deep learning in neural networks: An overview , 2014, Neural Networks.

[33]  Mark S. Nixon,et al.  Feature extraction & image processing for computer vision , 2012 .

[34]  Giancarlo Fortino,et al.  Lung cancer prediction using higher-order recurrent neural network based on glowworm swarm optimization , 2018, Neural Computing and Applications.

[35]  Andrew A. Borkowski,et al.  Lung and Colon Cancer Histopathological Image Dataset (LC25000) , 2019, ArXiv.

[36]  P. Baldi,et al.  Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. , 2018, Gastroenterology.

[37]  Aanchal Chaurasia,et al.  Convolution Neural Networks for diagnosing colon and lung cancer histopathological images , 2020, ArXiv.

[38]  S. Biswas,et al.  AI Doctor: An Intelligent Approach for Medical Diagnosis , 2018 .

[39]  Eric I-Chao Chang,et al.  Multi‐label classification for colon cancer using histopathological images , 2013, Microscopy research and technique.

[40]  Anil K. Jain.  Fundamentals of Digital Image Processing , 2018, Control of Color Imaging Systems.

[41]  P. Mohamed Shakeel,et al.  Automatic lung cancer detection from CT image using improved deep neural network and ensemble classifier , 2020, Neural Computing and Applications.

[42]  Jacob Cohen,et al.  Weighted kappa: Nominal scale agreement provision for scaled disagreement or partial credit. , 1968 .

[43]  Omar Cheikhrouhou,et al.  Light Deep Model for Pulmonary Nodule Detection from CT Scan Images for Mobile Devices , 2020, Wirel. Commun. Mob. Comput..

[44]  Sotirios A. Tsaftaris,et al.  Medical Image Computing and Computer Assisted Intervention , 2017 .

[45]  Biswajit Mishra,et al.  Image Compression Using 2D-Discrete Wavelet Transform on a Light Weight Reconfigurable Hardware , 2018, 2018 31st International Conference on VLSI Design and 2018 17th International Conference on Embedded Systems (VLSID).

[46]  R. Bowman.  Medical Electronics , 1959, Nature.

[47]  G. Lodwick,et al.  THE CODING OF ROENTGEN IMAGES FOR COMPUTER ANALYSIS AS APPLIED TO LUNG CANCER. , 1963, Radiology.