Breast cancer is the most prevalent form of cancer that can strike at any age; the higher the age, the greater the risk. The presence of malignant tissue has become more frequent in women. Although medical therapy has improved breast cancer diagnostic and treatment methods, still the death rate remains high due to failure of diagnosing breast cancer in its early stages. A classification approach for mammography images based on nonsubsampled contourlet transform (NSCT) is proposed in order to investigate it. The proposed method uses multiresolution NSCT decomposition to the region of interest (ROI) of mammography images and then uses Z-moments for extracting features from the NSCT-decomposed images. The matrix is formed by the components that are extracted from the region of interest and are then subjected to singular value decomposition (SVD) in order to remove the essential features that can generalize globally. The method employs a support vector machine (SVM) classification algorithm to categorize mammography pictures into normal, benign, and malignant and to identify and classify the breast lesions. The accuracy of the proposed model is 96.76 percent, and the training time is greatly decreased, as evident from the experiments performed. The paper also focuses on conducting the feature extraction experiments using morphological spectroscopy. The experiment combines 16 different algorithms with 4 classification methods for achieving exceptional accuracy and time efficiency outcomes as compared to other existing state-of-the-art approaches.

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In the modern world, due to the usage of high-power chemical-based cosmetics, climate change, and other major factors, skin cancer has been increasing among individuals. Skin cancer is considered as the most common malignant disorder, and there are more than a million cases being recorded with this disease every year. Extensive studies have already been performed to identify the risk factors and causative agents for skin cancer, including lifestyle changes and eatery patterns among individuals. The most common type of skin cancer is classified into basal cell carcinoma and squamous cell carcinoma. The researcher intends to conduct the research with the primary goal of determining the important factors in blockchain technology in the treatment of skin cancer in senior people. The application of new technologies such as blockchain has enabled offering better promises to health care professionals in addressing skin cancer in a more effective manner. These tools supported in evaluating the nature and severity of psoriasis has been regarded as much support for health care professionals in detecting skin cancer and offer better health care guidance for better living. The detection of melanomas supports the patient in enhancing the prognosis and support in discriminating between the melanomas and less impact lesions. The blockchain-based classification system offers more benefits and reduces the cost of detecting skin cancer in an effective manner. It also helps the medical professionals by assisting them in developing a custom diet plan for each patient on the basis of their health records and food intake. The researchers are focused on applying both the primary data sources and secondary data sources for performing the study. A detailed questionnaire is designed, and it is shared with the participants through university hospitals, support groups, etc. so as to gather the information. Nearly 156 respondents were chosen through nonprobability sampling, and the information was collected. The researcher performs critical descriptive analysis, and correlation analysis is performed to understand the overall association between the variables. The researchers intend to perform the study with the basic goal of understanding the critical factors in blockchain technology in skin cancer for elderly individuals. The major factors involved are enhanced data privacy, support in forecasting patterns, and enhanced medical services to patients complemented with personalized dietary assessment and recommendations. The result demonstrates that artificial intelligence-based blockchain technology allows for the efficient processing of huge amounts of data in order to complete the assigned task and correctly determine and predict the model.

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