Since histopathological images exist in various forms, performing segmentation on these images is tedious. While in cancer-free colon tissue, epithelial cells generally have an elliptical shape; their structure alters in a malignant tissue. This study proposes a technique consisting of colon biopsy image segmentation and a hybrid set of features for classification, and is evaluated on multiple databases with various levels of magnifications. This study presents a novel image segmentation method with multi-level thresholding based on Rényi's two-dimensional entropy with a cultural algorithm (2DR ${}_e$CA). Based on the entropy, elliptical epithelial cells, being the region of interest, are identified from the segmented background. After successful segmentation, shape descriptors are extracted with morphological operations. Two sets of texture features (grey-level co-occurrence matrix and block-wise elliptical local binary pattern) are calculated based on pre-processed grey-scale colon images. The proposed hybrid feature vector set, then concatenates the extracted features for training and testing with a random forest classifier. The proposed segmentation and classification model is evaluated by considering four data sets consisting of various colon images at different magnifications. In addition, it is evaluated by multiple performance measures and compared with existing techniques.

Any condition that damage or impedes the normal operation of the lungs is classified as a lung disease, and failure to identify and address it early on can potentially lead to false outcomes. To address this challenge, two innovative techniques are proposed for lung disease classification, supporting medical professionals to diagnose and provide preventive measures at an early stage. The Proposed Model 1 integrates a custom MobileNetV2L2 architecture, that builds upon the MobileNetV2 framework through fine-tuning and customization. This model incorporates a ridge or L2 regularizer within its dense layer to enhance its performance. The Proposed Model 2, custom CNN2 built on CNN as its foundational block, is fine-tuned with ELU as the activation function, replacing ReLU, and incorporates the ridge or L2 regularization technique. The proposed research utilizes two publicly available datasets: DS1(Data Set1), which is the Lung Disease 5-class dataset, and DS2(Data Set2), which is the Lung Disease 4-class dataset and are collected from Kaggle. The results from the Proposed Model 1 provide better performance than state-of-the-art techniques like EfficientNet B0, InceptionV3, ResNet, and InceptionResNetV2. It achieved a training accuracy of 99.53%, validation accuracy of 100%, and test accuracy of 95.51%. The proposed Model 2 provides outstanding performance, with a training accuracy of 96.79%, validation accuracy of 91.56%, and testing accuracy reaching 99.26% The proposed research serves as a valuable tool for Pulmonologists, providing a secondary opinion in the diagnostic process.

Cervical cancer is a major health concern for women worldwide, and early detection is essential for successful treatment. Since symptoms often do not appear until later stages, early screening is necessary. Machine learning can help classify cervical cancer risk by analyzing patient datasets and identifying the important factors that predict the likelihood of emerging cervical cancer. This paper evaluates six different machine-learning approaches for analyzing risk factors associated with cervical cancer using a dataset of 838 instances with 36 features. Results show that the SVM classifier performs the best, with an accuracy of 99.60% which emphasize the possibility of utilizing machine learning to enhance the precision of cervical cancer risk assessment. This can result in the development of better screening and prevention techniques for cervical cancer, which can be more effective in identifying and managing this disease.