Detection of Breast Cancer Using Infrared Thermal Images for Improved Accuracy by Using Random Forest and Multilayer Perceptron

At the present time, breast cancer is one of the most often diagnosed forms of cancer in females. Mammography is the most common form of screening imaging used to identify breast cancer in its earlier stages. Nevertheless, thermal infrared pictures (thermography) can be utilized to detect lesions in dense breasts. In this study, the typical areas reflect warmer temperatures than malignant areas. In this study, we offer a unique approach for modeling the temperature variations in normal and abnormal breasts by combining the Random forest and Multilayer perceptron techniques. The project aims to study the accuracy, sensitivity, and specificity of the infrared breast cancer images using infrared thermal images using random forest and multilayer perceptron algorithms and comparing the accuracy, specificity, and sensitivity. Materials and Methods: The information for this study was s gained from thermal images from Visual labs DMR-IR. The samples were considered as (N=60) for Random Forest and (N= 60) for MultiLayer Perceptron. Novel Matlab software is used to calculate accuracy, specificity, and sensitivity. Results: The result demonstrates the accuracy of the thermal breast images using SPSS software. A statistically insignificant difference exists, with Random Forest accuracy (92.5%) with specificity (90%) and with sensitivity (95%) and demonstrated a better outcome in comparison with Multilayer Perceptron accuracy (90%), specificity (91.6%) and sensitivity (88.3%). Conclusion: Random Forest gives better accuracy, specificity, and sensitivity than Multilayer Perceptron to detect breast cancer

Comparison of Dense Net and over Logistic Regression in Predicting Leukemia Classification with Improved Accuracy

This study compares the performance of densenet and support vector machines (SVMs) in the diagnosis of leukemia disease, with the aim of improving the accuracy of the classification results. Materials and Method The Kaggle website is where the dataset was found. The dataset consists of 20 samples per group in JPG files with a resolution of 96 dpi and 512×512 pixel size.The sample size is determined using a pretest power of 80%, a threshold of 0.05, and a confidence interval of 95%. Results: For leukemia, dense net is 96.5%, whereas logistic regression is 89%. The significance levels for Densenet and logistic regression are data with p=.000 (p<0.05) statistical significance difference respectively. Conclusion: Based on the findings, I believe that densenet performs superior to logistic regression

Detection of Breast Cancer Using Infrared Thermal Images for Improved Accuracy by Using Random Forest and Multilayer Perceptron

At the present time, breast cancer is one of the most often diagnosed forms of cancer in females. Mammography is the most common form of screening imaging used to identify breast cancer in its earlier stages. Nevertheless, thermal infrared pictures (thermography) can be utilized to detect lesions in dense breasts. In this study, the typical areas reflect warmer temperatures than malignant areas. In this study, we offer a unique approach for modeling the temperature variations in normal and abnormal breasts by combining the Random forest and Multilayer perceptron techniques. The project aims to study the accuracy, sensitivity, and specificity of the infrared breast cancer images using infrared thermal images using random forest and multilayer perceptron algorithms and comparing the accuracy, specificity, and sensitivity. Materials and Methods: The information for this study was s gained from thermal images from Visual labs DMR-IR. The samples were considered as (N=60) for Random Forest and (N= 60) for MultiLayer Perceptron. Novel Matlab software is used to calculate accuracy, specificity, and sensitivity. Results: The result demonstrates the accuracy of the thermal breast images using SPSS software. A statistically insignificant difference exists, with Random Forest accuracy (92.5%) with specificity (90%) and with sensitivity (95%) and demonstrated a better outcome in comparison with Multilayer Perceptron accuracy (90%), specificity (91.6%) and sensitivity (88.3%). Conclusion: Random Forest gives better accuracy, specificity, and sensitivity than Multilayer Perceptron to detect breast cancer

Comparison of Dense Net and over Logistic Regression in Predicting Leukemia Classification with Improved Accuracy

This study compares the performance of densenet and support vector machines (SVMs) in the diagnosis of leukemia disease, with the aim of improving the accuracy of the classification results. Materials and Method The Kaggle website is where the dataset was found. The dataset consists of 20 samples per group in JPG files with a resolution of 96 dpi and 512×512 pixel size.The sample size is determined using a pretest power of 80%, a threshold of 0.05, and a confidence interval of 95%. Results: For leukemia, dense net is 96.5%, whereas logistic regression is 89%. The significance levels for Densenet and logistic regression are data with p=.000 (p<0.05) statistical significance difference respectively. Conclusion: Based on the findings, I believe that densenet performs superior to logistic regression