Cancer Classification Using Machine Learning

The below listed topics are what we have developed.

1. Classification of Cancer Genome Atlas Glioblastoma Multiform (TCGA-GBM) Using Machine Learning Method

Keywords

Classification, Genome Atlas, TCGA-GBM, Machine Learning Approaches, Unbalanced Data

            A major objective of our research is to categorize the GBM data samples through the utilization of ML techniques. We performed an experimental analysis by using various supervised learning methods such as Support Vector Machine, Ad boost, Neural Network, and Decision Tree. We conclude that, Decision tree method provides greater outcomes in categorization process.

2. Classification of Breast Cancer Diagnosis Using Machine Learning Algorithms

Keywords

Component, breast cancer, random forest, XGB, logistic regression

            Several ML approaches are employed in our study to categorize the breast cancer into benign and malignant. Techniques including Logistic Regression, Decision Trees, Random Forest, KNN, Extreme Gradient Boosting and Support Vector Classifier are utilized. We carried out comparative analysis and examine these performances in terms of several metrics. We also examined the impact of PCA and Recursive Feature Elimination for dimensionality reduction.

3. Comparative Analysis of Machine Learning Methods for Multi-Label Skin Cancer Classification

Keywords

Skin cancer, SVM, DT, GNB, Multi-class

            Identification and categorization of multi-label skin cancer is the main goal of our article. We executed an efficient approach by utilizing ML methods and image processing techniques. To eliminate the unessential features from the data, we carried out preprocessing techniques. Several ML methods are employed on the dataset to evaluate the efficiency of every method. As a consequence, SVM, DT, and GNB achieved best end results compared to others.

4. Diagnosis and Classification of Breast Cancer Using Multiple Machine Learning Algorithms

Keywords

Multilayer Perceptron, WDBC Dataset

            By utilizing different ML approaches, we diagnosis and categorize the breast cancer into benign and malignant in our study. We compared various methods to find out the efficient method in categorization process. In that, SVM and Random Forest offers better outcomes. At last, we demonstrated that the employment of ML techniques helps in early diagnosis of breast cancer.

5. Skin Cancer Classification Based on Machine Learning Techniques

Keywords

Melanoma, k-means

            Our research suggested an innovative approach to contrast and identify the infected disease from nevus. Firstly we utilized Gaussian filter to eliminate the noise from the infected skin exist in the obtained image. To categorize the cancer as nevus or malignant, we employed the SVM method. To evaluate the performance of our recommended dissection method is the major aim of our research.

      6.Automatic Scikit-learn based detection and classification of Breast Cancer using Machine. / Learning techniques

Keywords

Naive Bayes, K- Nearest Neighbor, Artificial Neural Network, Breast cancer categorization, Breast cancer prediction, benign, malignant

            In our paper, we developed a framework by utilizing Scikit-learn to classify the breast cancer into malignant or benign. We employed various ML techniques such as Naive Bayes, Random Forest, KNN, Support Vector Machine, and Decision Tree on the dataset. We compared these techniques to evaluate the results. We stated that, because of early identification of breast cancer, the life span of cancer patients is increased by 5 years.

7. Colon Cancer Tissue Classification Using ML

Keywords

Colorectal cancer, Histopathological image classification, differential-box-count

            A categorization of colon cancer tissue is carried out in our approach by employing ML methodologies. Here, various kinds of colorectal tissues are utilized after preprocessing of data. We extracted the features by using Differential-Box-Count on all blocks of images. Several ML approaches like KNN, SVM, DT, RF, Extreme Gradient Boosting, and Gaussian Naive Bayes are applied on the dataset. Finally, Extreme Gradient Boosting achieved highest results.

8. BCM-VEMT: classification of brain cancer from MRI images using deep learning and ensemble of machine learning techniques

Keywords

Brain cancer, Convolutional neural network, Transfer learning, Ensemble of classifiers, MRI images

            To categorize the brain tumor from the MRI pictures, we recommended an improved technique denoted BCM-VEMT by utilizing DL and ensemble of ML methods. We categorized the brain cancer into four types. We extracted the deep features from the MRI pictures by constructing CNN method. At last, by integrating the findings of each ML methods, a weighted average ensemble method is utilized to attain effective end results.

9. Multimodal classification of breast cancer using feature level fusion of mammogram and ultrasound images in machine learning paradigm

Keywords

Feature level fusion, Mammogram, Multimodal image classification, Ultrasound

            A new semi-automated multimodal categorization framework is suggested in our article to classify the breast tumor by integrating mammogram and ultrasound image features. We extracted the features and selected the most important features by employing statistical significance analysis. We categorized the tumor into malignant or benign. To eliminate the noises in images, we utilized filtering technique. As a result, SVM provides greater outcomes.

10. Multi-class Classification for Breast Cancer with High Dimensional Microarray Data Using Machine Learning Classifier

Keywords

Boruta’s algorithm, Microarray, Multinomial logistics regression

            A multi-class categorization of breast cancer by utilizing large dimensional microarray data is the main concentration of our research. We selected the essential microarray biomarkers by employing Boruta’s feature selection technique. We investigated various ML approaches such as SVM, multinomial logistic regression, Naïve Bayes, and random forest in terms of several metrics. As a consequence, SVM outperform other methods in multi-classification process.