CICADA (UCX): A novel approach for automated breast cancer classification through aggressiveness delineation

Abstract

Breast cancer remains one of the leading causes of mortality worldwide, with current classification and segmentation techniques often falling short in accurately distinguishing between benign and malignant cases. The study both emphasize the novel approach, CICADA (UCX), specifically designed for breast segmentation with a focus on delineating aggressiveness. While the title highlights segmentation, the abstract expands on this by detailing the model's effectiveness in enhancing diagnostic precision in classifying aggressive tumor characteristics. Breast cancer segmentation pertains to the delineation of malignant tissue borders in medical imaging. The objective is to precisely delineate the malignant area from healthy tissues, facilitating reliable evaluation of tumor attributes like location, size, and form. Historically, manual segmentation by radiologists has been the benchmark; however, it is labor-intensive and susceptible to fluctuation among different observers and within the same observer. With the advancement of medical imaging technologies, there is an increasing demand for automated or semi-automatic systems capable of performing segmentation with efficiency and precision. These strategies seek to minimize human error, enhance reproducibility, and expedite diagnosis, so enabling prompt treatment. A significant problem in breast cancer segmentation is the variability in tumor morphology among various patients and imaging techniques. Neoplasms exhibit considerable variability in dimensions, morphology, and density, complicating the formulation of a universal approach. Moreover, elements like breast tissue density, which might hinder tumor appearance in mammograms, further complicate segmentation. A further barrier is the necessity for extensive, meticulously annotated datasets to train and test machine learning models, as medical picture annotation is labor-intensive and demands specialized expertise. Notwithstanding these obstacles, automated breast cancer segmentation has demonstrated significant potential in clinical applications. It assists radiologists in swiftly and precisely identifying questionable areas, resulting in earlier diagnosis and enhanced patient outcomes. Automated devices can aid in treatment planning by delivering accurate measures of tumor size and location, which are essential for establishing suitable surgical or radiation methods. This study addresses these limitations by introducing CICADA (UCX), which aims to enhance diagnostic precision and operational efficiency in clinical applications. The present study focuses on the creation and assessment of a sophisticated medical picture segmentation model, called Cheetah Inspired Convex Adaptive Discriminator Algorithm with Unet Convenet Xt CICADA (UCX), by contrasting it with the most advanced techniques currently in use. With a mean IOU of 96.34 %, a Dice Coefficient/F1-Score of 99.6461 %, and an AUC of 99.88 %, the suggested model performs quite well. The study incorporates various feature selection techniques like Particle Swarm Optimisation, Dragon Fly, Grey Wolf and our proposed novel technique named as CICADA (UCX). Through a thorough comparison analysis using many approaches, the paper highlights the advantages of CICADA (UCX) for medical picture segmentation. The study advances the area by offering fresh perspectives on segmentation accuracy, with a focus on obtaining a high Dice Coefficient/F1-Score. The results highlight how CICADA (UCX) has the ability to greatly improve medical image analysis and enable more precise and effective diagnosis. The CICADA (UCX) model, a revolutionary approach to medical picture segmentation, is presented in this study, which is a significant improvement over other existing technique. The model outperforms state-of-the-art methods in a thorough comparison investigation, showing higher performance across important assessment measures including mean IOU, Dice Coefficient/F1-Score, and AUC. Notably, the model scores a remarkable 99.6461 % Dice Coefficient/F1-Score, demonstrating accurate medical structural delineation. An important aspect of medical imaging applications is segmentation accuracy, which is greatly improved by this study. The results point to possible improvements in operational efficiency and diagnostic accuracy, which would be beneficial to patients as well as medical personnel. This discovery has significance for improving medical picture segmentation techniques and promoting technological developments in medical imaging and computer-aided diagnosis.