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Human brain tumors are now the most serious and horrible diseases for people, causing certain deaths. The patient’s life also becomes more complicated over time as a result of the brain tumor. Thus, it is essential to find tumors early to safeguard and extend the patient’s life. Hence, new improvements are highly essential in the techniques of brain tumor detection in medical areas. To address this, research has introduced automatic brain tumor prediction using Pteropus unicinctus optimization on deep neural networks (PUO-deep NNs). Initially, the data are gathered from the BraTS MICCAI brain tumor dataset and preprocessing and ROI extraction are performed to remove the noise from the data. Then the extracted RoI is forwarded to the fuzzy c-means (FCM) clustering to segment the brain image. The parameters of the FCM tune the PUO algorithm so the image is segmented into the tumor region and the non-tumor region. Then the feature extraction takes place on ResNet. Finally, the deep NN classifier successfully predicted the brain tumor by utilizing the PUO method, which improved the classifier performance and produced extremely accurate results. For dataset 1, the PUO-deep NN achieved values of 87.69% accuracy, 93.81% sensitivity, and 99.01% specificity. The suggested PUO-deep NN also attained the values for dataset 2 of 98.49%, 98.55%, and 95.60%, which is significantly more effective than the current approaches.

Breast cancer is one among the dreadful cancer which is caused due to formation in breast cells. Earlier recognition of breast cancer is most required in the medical field to avoid the dangerous threat to human life. This is carried out in the existing work, namely Predictive Modeling Technique (PMT). Existing work cannot handle the database with noises properly which might lead to inaccurate prediction outcome. These problems are addressed by introducing Deep Learning-based Breast Cancer Disease Prediction Framework (DLBCDPF). The proposed research framework objective is to present the structures for the disease diagnosis in a further accurate way. In this work, feature selection is achieved through optimization algorithm, namely ranking-based bee colony approach by which the most optimal feature is chosen from the training dataset. The fitness values considered in this work for optimal feature selection are F-score values. Each feature’s F-score and N numbers of feature’s F-score are arranged in a descending manner; in addition, feature subset with more than one feature are produced. In this phase, diagnosis of various stomach-related problems is done through a unique hybridized classification methodology. In this hybridization methodology, clustering is accomplished before classification, and data pruning is attained in every classification iteration. This leads to improved classification accuracy owing to efficient diagnosis. The clustering is attained by fuzzy C-means clustering, and classification is done using the improved deep neural network. The entire research analysis is carried out in python platform for breast cancer dataset from which it is substantiated that the suggested research work tends to outperform in an enhanced way than prevailing work.

In this paper, an image edge detection algorithm based on FCM clustering and wavelet transform is presented. By this algorithm, we can not only detect the entire edge of original image, but also detect the edge of irregular sub-image that satisfies some characteristics. In the end, we compare it with Prewitt algorithm, Canny algorithm and Laplacia algorithm respectively. Experimental results show that the algorithm proposed in this paper has better performance in image edge detection.