Lung nodule can cause lung cancer and so researchers do their best to detect those nodules in their early stages. Machine learning algorithms are used to detect lung nodules in a short time with high accuracy. Random Forest (RF) is a remarkable ensemble machine learning algorithm can be used to classify medical images, recognize different pathologies and detect deficiencies based on selected input features. The paper proposes a model that enables early detection and localization for lung nodule from CT images and propose RF optimization and analysis the effect of the feature groups on the classification accuracy. Processing was applied on features extracted from CT images to optimize the RF output. In previous work, local features such as Haar features gave better results than region-based features. In the proposed model after applying a novel ANDing technique in preprocessing step these region-based features gave better results and the model accuracy enhanced. By combining global and local features the model classification results and accuracy are greatly improved. Experiments were made using 214 cases with total 2124 CT slices downloaded from the publicly available LIDC database. After applying preprocessing using novel technique, 119 features are calculated and extracted from each pixel in the CT image. Post-processing is made on the extracted features to refine the learner input data. Feature dimensionality reduction was applied by dividing features into 5 different feature sets and select best scored results. Finally, when comparing with previous work, RF is optimized, true positive rate is increased by 8.66% and false positive rate is decreased by 4.4% which led to better localization and accuracy increased by 5.47%. Best achieved results were 96.41%, 95.98% and 96.20% for sensitivity, specificity and accuracy respectively when tuning RF with 80 trees and 0.04 for in bag fraction. Results from RF were compared with other methodologies such as KNN, SVM, CNN and deep learning and RF proved to give best accuracy as mentioned in the discussion section. 

Features processing for random forest optimization in lung nodule localization

Abstract In the lung computer-aided detection (Lung CAD) system, the region of interest (ROI) of lung nodules has more false positives, making the imbalance between positive and negative (true positive and false positive) samples more likely to lead to misclassification of true positive nodules, a cost-sensitive multikernel learning support vector machine (CS-MKL-SVM) algorithm is proposed. Different penalty coefficients are assigned to positive and negative samples, so that the model can better learn the features of true positive nodules and improve the classification effect. To further improve the detection rate of pulmonary nodules and overall recognition accuracy, a score function named F -new based on the harmonic mean of accuracy ( ACC ) and sensitivity ( SEN ) is proposed as a fitness function for subsequent particle swarm optimization (PSO) parameter optimization, and a feasibility analysis of this function is performed. Compared with the fitness function that considers only accuracy or sensitivity, both the detection rate and the recognition accuracy of pulmonary nodules can be improved by this new algorithm. Compared with the grid search algorithm, using PSO for parameter search can reduce the model training time by nearly 20 times and achieve rapid parameter optimization. The maximum F -new obtained on the test set is 0.9357 for the proposed algorithm. When the maximum value of F -new is achieved, the corresponding recognition ACC is 91%, and SEN is 96.3%. Compared with the radial basis function in the single kernel, the F -new of the algorithm in this paper is 2.16% higher, ACC is 1.00% higher and SEN is equal. Compared with the polynomial kernel function in the single kernel, the F -new of the algorithm is 3.64% higher, ACC is 1.00% higher and SEN is 7.41% higher. The experimental results show that the F -new, ACC and SEN of the proposed algorithm is the best among them, and the results obtained by using multikernel function combined with F -new index are better than the single kernel function. Compared with the MKL-SVM algorithm of grid search, the ACC of the algorithm in this paper is reduced by 1%, and the results are equal to those of the MKL-SVM algorithm based on PSO only. Compared with the above two algorithms, SEN is increased by 3.71% and 7.41%, respectively. Therefore, it can be seen that the cost sensitive method can effectively reduce the missed detection of nodules, and the availability of the new algorithm can be further verified. 

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Improved cost-sensitive multikernel learning support vector machine algorithm based on particle swarm optimization in pulmonary nodule recognition

Nowadays, the proficiency of Computer-Aided Diagnosis systems for early diagnosis of malignant nodules in baseline Computed Tomography (CT) scan of chest crucially depends on the authenticity of the segmented nodule. In this study, the authors introduce a new morphological feature called solidity radius (SR). They then employ this feature in the new segmentation framework for the automatic segmentation of nodule and the attached vessels around the seed point on the nodule, delineated by an expert. In the framework, they extract the SR and the curvature features and employ them to determine the candidate pixels of the nodule. They then use the convex-hull image of the candidate pixels to surround the nodule area. Afterward, using the region growing on the Hessian-based vesselness enhancement map, the attached vessels are labelled. Finally, they apply the traditional solidity feature of the segmented nodule and the pattern of the related skeleton to prune the false positive pieces. They validate the introduced approach on two datasets, including 56 and 481 CTs (containing 1205 nodules). They show the proficiency of their SR-based approach compared to the state-of-the-art methods with average Dice Similarity Coefficients of 77.98 and 77.47% for the two datasets, respectively.

https://ietresearch.onlinelibrary.wiley.com/doi/pdf/10.1049/iet-ipr.2019.1054

Segmentation of the pulmonary nodule and the attached vessels in the CT scan of the chest using morphological features and topological skeleton of the nodule

Deep learning approach for segmentation and classification of blood cells using enhanced CNN

High death rate in women is caused mainly due to breast cancer. Image processing, as well as data mining and machine learning approaches, has all aided in the development of autonomous cancer detection systems. One employed machine learning classification algorithms to distinguish between benign and malignant tumors. Digital mammography is found to be the successful tool for detecting cancer in women who have no symptoms and diagnosing cancer in women who have symptoms such as discomfort in a lump or nipple discharge. As a result, one can use convolutional neural networks (CNN) in the suggested system, and OpenCV was performed on a data set from the UCI repository (Laurance in Breast Cancer Cases Rise 80% since Seventies; BREAST CANCER, The Independent, London, 2006). The accuracy, precision, sensitivity, specificity, and false positive rate of each algorithm are analyzed and evaluated. 

Early Detection of Breast Cancer Using CNN

Lung cancer is the prime factor in cancer related deaths due to increasing rate of smoking and air pollution. The survival rate of cancer patients increases to 52% if it is localized, and decreases to 4% if it is metastasized. The existing system use simple thresholding approach and pattern recognition method to segment lung and identify nodules in the lung computerized tomography (CT) images. But quality of this process is affected by the image acquisition protocols, scanner types, and inhomogeneity of intensities in the lung region. In order to overcome the drawbacks of the previous process and for separation of nodules from non-nodules, there is a need for an automatic computer aided detection system. This paper proposes an automatic method for detecting nodules from patient lung CT images. In this automatic approach first step is a lung segmentation to differentiate lung and background and make the detection of nodule simple. Then the lung region is segmented by Contextual Clustering based region growing method. Vector quantization method is used to identify the nodules present in the lungs. False positive reduction is done by using various combination of rule based filtering, support vector machine, k-Nearest Neighbours and Random Forest classifier. The performance of this method is evaluated in terms of accuracy, sensitivity and specificity.

H. Kareemulla

Papers

Automatic Detection of Lung Cancer Nodules in Computerized Tomography Images