Data Mining - Concepts and Techniques.

Machine Learning is modern and highly sophisticated technological applications became a huge trend in the industry. Machine Learning is Omni present and is widely used in various applications. It is playing a vital role in many fields like finance, Medical science and in security. Machine learning is used to discover patterns from medical data sources and provide excellent capabilities to predict diseases. In this paper, we review various machine learning algorithms used for developing efficient decision support for healthcare applications. This paper helps in reducing the research gap for building efficient decision support system for medical applications.

Machine Learning in Healthcare: A Review

Learning distinctive filters for COVID-19 detection from chest X-ray using shuffled residual CNN.

Computer-aided systems for skin lesion diagnosis is a growing area of research. Recently, researchers have shown an increasing interest in developing computer-aided diagnosis systems. This paper aims to review, synthesize and evaluate the quality of evidence for the diagnostic accuracy of computer-aided systems. This study discusses the papers published in the last five years in ScienceDirect, IEEE, and SpringerLink databases. It includes 53 articles using traditional machine learning methods and 49 articles using deep learning methods. The studies are compared based on their contributions, the methods used and the achieved results. The work identified the main challenges of evaluating skin lesion segmentation and classification methods such as small datasets, ad hoc image selection and racial bias.

https://www.mdpi.com/2075-4418/11/8/1390/pdf

Machine Learning and Deep Learning Methods for Skin Lesion Classification and Diagnosis: A Systematic Review

Melanoma remains the most harmful form of skin cancer. Convolutional neural network (CNN) based classifiers have become the best choice for melanoma detection in the recent era. The research has indicated that classifiers based on CNN classify skin cancer images equivalent to dermatologists, which has allowed a quick and life-saving diagnosis. This study provides a systematic literature review of the latest research on melanoma classification using CNN. We restrict our study to the binary classification of melanoma. In particular, this research discusses the CNN classifiers and compares the accuracies of these classifiers when tested on non-published datasets. We conducted a systematic review of existing literature, identifying the literature through a systematic search of the IEEE, Medline, ACM, Springer, Elsevier, and Wiley databases. A total of 5112 studies were identified out of which 55 well-reputed studies were selected. The main objective of this study is to collect state of the art research which identify the recent research trends, challenges and opportunities for melanoma diagnosis and investigate the existing solutions for the diagnosis of melanoma detection using deep learning. Moreover, proposed taxonomy for melanoma detection has been presented that summarizes the broad variety of existing melanoma detection solutions. Lastly, proposed model, challenges and opportunities have been presented which helps the researchers in the domain of melanoma detection.

/pdf/malignant-melanoma-classification-using-deep-learning-1f9b6cq3y0.pdf

Malignant Melanoma Classification Using Deep Learning: Datasets, Performance Measurements, Challenges and Opportunities

Breast cancer is one of the second leading causes of cancerdeath in women. Despite the fact that cancer is preventable and curable in primary stages, the huge number of patients are diagnosed with cancer very late. Conventional methods of detecting and diagnosing cancer mainly depend on skilled physicians, with the help of medical imaging, to detect certain symptoms that usually appear in the later stages of cancer [1]. The objective of this paper is to find the smallest subset of features that can ensure highly accurate classification of breast cancer as either benign or malignant. Then a comparative study on different cancer classification approaches viz. Naïve Bayes, Support Vector Machine and Ensemble classifiers is conducted where the time complexity of each of the classifier is also measured. Here, Naïve Bayes classifier is concluded as the best classifier with lowest time complexity as compared to the other two classifiers.

/pdf/study-and-analysis-of-breast-cancer-cell-detection-using-nr1hfkycif.pdf

Study and Analysis of Breast Cancer Cell Detection using Naïve Bayes, SVM and Ensemble Algorithms

Melanoma is one of the four major types of skin cancers caused by malignant growth in the melanocyte cells. It is the rarest one, accounting to only 1% of all skin cancer cases. However, it is the deadliest among all the skin cancer types. Owing to its rarity, efficient diagnosis of the disease becomes rather difficult. Here, a deep depthwise separable residual convolutional algorithm is introduced to perform binary melanoma classification on a dermoscopic skin lesion image dataset. Prior to training the model with the dataset noise removal from the images using non-local means filter is performed followed by enhancement using contrast-limited adaptive histogram equilisation over discrete wavelet transform algorithm. Images are fed to the model as multi-channel image matrices with channels chosen across multiple color spaces based on their ability to optimize the performance of the model. Proper lesion detection and classification ability of the model are tested by monitoring the gradient weighted class activation maps and saliency maps, respectively. Dynamic effectiveness of the model is shown through its performance in multiple skin lesion image datasets. The proposed model achieved an ACC of 99.50% on international skin imaging collaboration (ISIC), 96.77% on PH2, 94.44% on DermIS and 95.23% on MED-NODE datasets.

https://ietresearch.onlinelibrary.wiley.com/doi/epdf/10.1049/iet-ipr.2018.6669

Diagnosis of melanoma from dermoscopic images using a deep depthwise separable residual convolutional network

An unusual mass of tissue in which some cells multiplies and grows uncontrollably is called brain tumor. It starts growing inside the skull and interpose with the regular functioning of the brain. It needs to be detected at an early stage using MRI or CT scanned images when it is as small as possible because the tumor can possibly result to cancer [1]. This paper, mainly focuses on detecting and localizing the tumor region existing in the brain by proposed methodology using patient's MRI images. The proposed methodology consists of three stages i.e. pre-processing, edge detection and segmentation. Pre-processing stage involves converting original image into a grayscale image and removes noise if present or crept in. This is followed by edge detection using Sobel, Prewitt and Canny algorithms with image enhancement techniques. Next, segmentation is applied to clearly display the tumor affected region in the MRI images. Finally, the image is clustered using the k-means algorithm. Here we have used MATLAB for the development of the project.

Brain tumor detection based on segmentation using MATLAB

One of the most common and leading cause of cancer death in human beings is lung cancer. The advanced observation of cancer takes the main role to inflate a patient’s probability for survival of the disease. This paper inspects the accomplishment of support vector machine (SVM) and logistic regression (LR) algorithms in predicting the survival rate of lung cancer patients and compares the effectiveness of these two algorithms through accuracy, precision, recall, F1 score and confusion matrix. These techniques have been applied to detect the survival possibilities of lung cancer victims and help the physicians to take decisions on the forecast of the disease.

https://www.ijcaonline.org/archives/volume174/number2/hazra-2017-ijca-915325.pdf

Predicting Lung Cancer Survivability using SVM and Logistic Regression Algorithms

Pneumonia is an infection that inflames the air sacs in one or both lungs. The air sacs may fill with fluid or pus, causing cough with phlegm or pus, fever, chills, and difficulty in breathing. Pneumonia can be caused by a variety of organisms such as bacteria, viruses, and fungi. Apart from causing difficulty in respiration, pneumonia can cause other complications such as bacteremia, lung abscess, and pleural effusion, among countless others. This paper presents a novel automated method for efficient and accurate pneumonia diagnosis from chest X-ray(CXR) images. Also, this model utilizes an efficient technique for noise reduction using bilateral filtering for edge preservation and optimum enhancement of the images using contrast-limited adaptive histogram equalization(CLAHE) to aid the detection of pneumonia clouds in the CXR images. The proposed model explores the benefits of deep residual learning along with separable convolution algorithm to achieve a classification accuracy of \(98.82\%\) and AUROC score of 0.99726 for diagnosing the disease. For cross-validation of the model, gradient-weighted class activation map(Grad-CAM) and saliency map visualization are used as a measure to verify the performance of the model along with localization of the affected regions in the CXR images.

Animesh Hazra

Papers

Study and Analysis of Breast Cancer Cell Detection using Naïve Bayes, SVM and Ensemble Algorithms

Diagnosis of melanoma from dermoscopic images using a deep depthwise separable residual convolutional network

Brain tumor detection based on segmentation using MATLAB

Predicting Lung Cancer Survivability using SVM and Logistic Regression Algorithms

A Novel Method for Pneumonia Diagnosis from Chest X-Ray Images Using Deep Residual Learning with Separable Convolutional Networks