Method and system for manufacturer and type identification of a medical implant

A system, comprising:

• one or more hardware processors;
• a communication interface;
• and
• a memory storing a plurality of instructions, wherein the plurality of instructions cause the one or more hardware processors to:
• collect an X-ray image of a subject as input;
• extract, via a 3-block classifier executed by the one or more hardware processors, a plurality of features of at least one implant in the X-ray image, wherein the 3-block classifier extracts the plurality of features using a) an encoder-decoder block, b) a convolution dense block, and c) a classification block;
• compare the extracted plurality of features with a set of manufacturer specifications;
• and
• identify type and manufacturer of the at least one implant, based on a match found for the extracted plurality of features with the manufacturer specifications.

A 3-block classifier, comprising:

• an encoder-decoder block;
• a convolution dense block;
• and
• a classification block, wherein
• an encoder section of the encoder-decoder block comprises a plurality of convolution blocks arranged in a sequence, wherein a first convolution block in the plurality of convolution blocks comprises of a 2D convolution layer,
• a leakyRelu layer, and a Dropout layer, and each subsequent convolution block among the plurality of convolution blocks comprises the 2D convolution layer,
• the leakyRelu layer, the Dropout layer, and a batch normalization layer,
• a decoder section of the encoder-decoder block comprises a plurality of 2D transposed convolution up-sampling blocks arranged in a sequence, wherein each of the plurality of 2D transposed convolution up-sampling blocks comprises of a convolutional 2D transpose layer, a batch normalization layer, a leakyRelu layer, and a Dropout layer,
• the convolution dense block comprises a convolution block and a plurality of dense blocks, wherein the convolution block comprises 256 filters, a leakyRelu layer, and a batch normalization layer, and each of the plurality of dense blocks comprises a dense layer, the leakyRelu layer, and the batch normalization layer, and wherein, the convolution block extracts one or more finer features of the at least one implant using the 256 filters and one or more feature maps, and features of each of the plurality of dense blocks is combined with data from an activation layer using a concatenation block,
• a sobel filter is used for capturing a plurality of edge-based features of the at least one implant, wherein the plurality of edge-based features comprises one or more of edges of the at least one implant, one or more screws present in the at least one implant, one or more domes of the at least one implant, and pointed bottom shape of implant,
• the classification block classifies the extracted plurality of features to a set of pre-defined feature classes, and
• a supervised contrastive loss function is used to eliminate a class imbalance problem while identifying the type and manufacturer of the at least one implant, wherein while calculating loss of one class type the supervised contrastive loss function considers embeddings of one or more other classes.