Summary Human liver disease is a sort of illness that starts in the ovaries and is particularly dangerous for women. As a consequence, aberrant cells develop that have the potential to spread to other parts of the body. Liver disease is a serious disorder that affects women's ovaries and is difficult to identify early on, which is why it is still one of the leading causes of mortality. The significance of unequivocal confirmation of intrinsic and typical components in establishing new frameworks to detect and eliminate danger is substantial. In this study, we present a hybrid soft computing approach for early diagnosis of liver problems (EDLD‐HS). For high and low level feature extraction models, we first employ an improved ant swarm optimization (IASO) approach. Then, for optimum feature selection, we develop a modified whale search optimization (MWSO) technique that combines features that may reflect both texture patterns and semantic backdrop scattered in the data. After that, we employed a hybrid swallow swarm intelligent‐deep neural network (HSSI‐DNN) classifier to determine the stage of liver illness. Finally, we used MATLAB R2014a to test our proposed EDLD‐HS method using well‐known benchmark datasets including BUPA, ILPD, and MPRLPD. The simulation findings are compared to existing state‐of‐the‐art methodologies in terms of accuracy, specificity, sensitivity, precision, recall, F1‐score, G‐mean, and area under curve (AUC). The detection accuracy of the proposed HSSI‐DNN classifier is 83.26% (BUPA), 84.26% (ILPD), and 91.23% (ILPD) (MPRLPD).

Summary Liver disease is a form of sickness that starts in the ovaries and is particularly dangerous for women. As a result, aberrant cells develop that have the potential to spread to other parts of the body. Liver disease is a sort of risky improvement that impacts ovaries in ladies, and is hard to perceive at early phase because of which it stays as one of the guideline wellsprings of illness end. Unquestionable confirmation of intrinsic and typical parts is immense in making novel frameworks to perceive and ruin danger. Here, using hybrid machine learning approaches, we propose an early detection and classification of liver disease (EDCLD) in ultrasound images. This study is aimed at detecting and evaluating liver disease, which may be benign or malignant depending on the cancer type. To begin with, we use an improved ant swarm optimization algorithm for both high and low level feature extraction models. For liver disease classification, we used hybrid swallow swarm intelligent‐deep neural network (HSSI‐DNN) classifier to identify the disease stage. By using training and testing liver datasets, we are improving the accuracy of liver disease detection with several stages. Second, we present a search optimization for whales algorithm for cascaded and segmentation as a single fusion feature capable of representing both the texture patterns and semantic background distributed in the image. Third, the dividend yields are commitment to the HSSI‐DNN classifier used to detect the “Normal” and “Diseases” from input image. The proposed HSSI‐DNN classifier performance outperformed the existing classifier in terms of accuracy (0.98), specificity (0.98), and sensitivity (1). The proposed EDCLD technique implemented in MATLAB.