InceptionV3 in Medical Imaging: Enhancing Precision in Acute Lymphoblastic Leukaemia Diagnosis
Retinderdeep Singh, Neha Vaishnavi Sharma, Priyanshi Aggarwal, Mukesh Kumar Singh, Kanegonda Ravi Chythanya · 2024
Acute lymphoblastic leukaemia (ALL) is a group of blood malignancies that need very sophisticated detection methods and constitute a major danger to public health across the world. With the inceptionV3 model developed and implemented, this research study significantly advances blood cancer diagnostics. This model is built on top of a massive Blood Cells Cancer (ALL) dataset that includes a spectrum of cellular morphologies linked to ALL. This dataset and deep learning approaches were used to train the model being considered. A key component in enabling prompt and successful medical intervention, the major objective of this research activity is to enhance the precision and dependability of blood cancer diagnosis. Images of blood cells may reveal subtle aberrations and patterns, which may be recognised using a fine-tuned inceptionV3 model. The inceptionV3 model can use this to better differentiate between healthy and cancerous cells. The training procedure involves refining the model’s parameters to reach better degrees of sensitivity and specificity. After the training phase is over, the suggested inception model achieves an astonishing accuracy rate of 98.46%, demonstrating an excellent level of performance. The model demonstrates remarkable accuracy in diagnosing blood cancer with a high degree of precision, highlighting its potential as a crucial diagnostic tool in clinical settings.