Patient-Centric Breast Tumour Analysis: DenseNet201-Based Prediction and Personalized Recommendations
Mona Devi, Arvind S. Kapse, Golla Akhila, Ananya Ananya, Ankita S, Pranati Parashar · 2024
Breast cancer is the most important problem in the healthcare world that still requires the most advanced methods for accurate prediction and personalized treatment recommendations. This study represents the best integrated, in-depth analysis of adult content in studies on breast cancer. Our work leverages the powerful framework of Densenet201 to identify complex patterns in clinical data, enabling more accurate and precise predictions of cancer. The model uses large patient datasets and demonstrates better information and specificity, demonstrating its potential as a powerful diagnostic tool. Furthermore, by including self-awareness, this research goes beyond conventional prediction models. This tailored strategy not only increases the effectiveness of the treatment plan but furthermore considers each patient's unique needs and responses. The results show a significant improvement in predicting breast cancer compared to existing models, demonstrating the effectiveness of Densenet201 in this area. The significance of this study extends to advances in personalized medicine, supportive adaptations, and patient specific cancer diagnosis and treatment.